Framework for stream clustering of trajectories based on temporal micro clustering technique

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(1)UNIVERSITI PUTRA MALAYSIA FRAMEWORK FOR STREAM CLUSTERING OF TRAJECTORIES BASED ON TEMPORAL MICRO CLUSTERING TECHNIQUE. MUSAAB RIYADH ABDULRAZZAQ. FSKTM 2018 69.

(2) PM. IG. H. T. U. FRAMEWORK FOR STREAM CLUSTERING OF TRAJECTORIES BASED ON TEMPORAL MICRO CLUSTERING TECHNIQUE. R. By. ©. C. O. PY. MUSAAB RIYADH ABDULRAZZAQ. Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia, in Fulfillment of the Requirements for the Degree of Doctor of Philosophy. March 2018 1.

(3) COPYRIGHT. PM. All material contained within the thesis, including without limitation text, logos, icons, photographs, and all other artwork, is copyright material of Universiti Putra Malaysia unless otherwise stated. Use may be made of any material contained within the thesis for non-commercial purposes from the copyright holder. Commercial use of material may only be made with the express, prior, written permission of Universiti Putra Malaysia.. ©. C. O. PY. R. IG. H. T. U. Copyright © Universiti Putra Malaysia. i.

(4) DEDICATION. ©. C. O. PY. R. IG. H. T. U. PM. Dedicated to my amazing wife Zainab for her love, support and encouragement, my daughter Layan, my parents Riyadh and Badeeah, my brother Saad and my sisters May, Nada, and Dina for their love and affection. ii.

(5) Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfillment of the requirement for the degree of Doctor of Philosophy. FRAMEWORK FOR STREAM CLUSTERING OF TRAJECTORIES BASED ON TEMPORAL MICRO CLUSTERING TECHNIQUE. PM. By. March 2018. T. : Associate Professor Norwati Mustapha, PhD : Computer Science and Information Technology. H. Chairman Faculty. U. MUSAAB RIYADH ABDULRAZZAQ. ©. C. O. PY. R. IG. In recent years, spatio-temporal data has rapidly increased due to the tremendous prevalence of geolocation devices such as Global Positioning System, mobiles, and motion sensors. In some scenarios, spatio-temporal data are received in a streamed manner. Clustering of the stream data is a challenging task due to single pass over the data, the unbounded size of data stream, and the limited processing time and memory space. However, it is a vital process for many applications such as traffic management, studying of animal behavior, and weather forecasting. Many existing algorithms for stream clustering of trajectory stream data exploit the time window technique which partition trajectory stream data into time-bins and cluster the segments in each timebin separately. Initiating clustering from scratch in each time-bin and not considering the relationships between the objects from two consecutive time-bins lead to creating redundant micro clusters centralizes in the border area between two adjacent time bins. This is because, the clustering process which exploits the time window technique creates new micro clusters for some trajectory segments at the start of each time-bin even though these segments are too close (within distance threshold) to the micro clusters at the end of previous time-bin . It is true that most similar micro clusters at two consecutive time-bins can be merged to reduce memory space but creating redundant micro clusters will dramatically slow down the clustering task. On the other hand, trajectories preprocessing such as segmentation and noise points filtering is a vital step which precedes the mining task. It aims to reduce the size of the trajectory to minimize clustering time. Most of the existing algorithms consider the noise filtering step precedes trajectory segmentation step which mean that there is no preprocessing method to partition trajectory into set of segments and remove noise points in real time with low computational cost.. i.

(6) PM. As a response towards the limitations of time window technique and the offline preprocessing methods, a framework for stream clustering of trajectories based on temporal micro clustering technique (SCT-TMC) is proposed. The framework consists of two stages: the preprocessing stage and the stream clustering stage. In the preprocessing stage, the On-Line Noise Filtering Algorithm for Trajectory Segmentation Based on Minimum Description Length concept (ONF_TRS) is proposed to achieve trajectory segmentation and remove noise points in real time with low computational cost. The minimum description length is an important concept in information theory and computational learning in which the best hypothesis for a given data set is the one that leads to the best compression of the data.. ©. C. O. PY. R. IG. H. T. U. The stream clustering stage consists of two phases: the online and the offline phases. In the online phase, the stream clustering algorithm for trajectories based on the lifespan of the cluster is proposed (CC_TRS) to overcome the limitations of the time window technique. The clustering algorithm consists of two components: the temporal micro-clusters generation and the temporal micro clusters merging. The temporal micro cluster data structure is proposed in CC_TRS algorithm to store the summarized information for each group of similar segments. The algorithm assigns a life time for each newly created temporal micro clusters so that the new incoming batch of trajectories segments will interact only with the non-expired temporal micro clusters. This is because the expired temporal micro clusters become far from segments at current time either spatially or temporally which in turn, minimizes the searching time for the nearest temporal micro cluster. When the size of the temporal micro clusters is exceeded a given memory space, the most similar temporal micro-clusters will be merged to save memory. On the other hand, the offline phase is evoked when the user requests to view the overall clustering results. The DBSCAN algorithm is used to perform the macro clustering task by replacing the distance between trajectories segments with the distance between the temporal micro-clusters. The DBSCAN is a density based clustering approach which is convenient for stream clustering due to the properties: firstly, it does not require to specify the number of clusters in advance. Secondly, the algorithm can find arbitrarily shaped clusters. Finally, it is robust to noise and outliers. The suggestion of the temporal micro cluster data structure in the online phase leads to the expansion of the functionality of the macro clustering phase. The proposed functions for the offline phase are the detection of spatial and spatiotemporal outliers and the macro clustering. A comprehensive experimental analysis was conducted to evaluate the efficiency and effectiveness of the proposed algorithms (ONF-TRS, CC-TRS). The results shows that the algorithm ONF-TRS has low computational cost and high compression rate as compared with the existing works. Furthermore, the CC-TRS improves and speeds up the clustering task as compared with the latest works.. ii.

(7) Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai memenuhi keperluan untuk ijazah Doktor Falsafah RANGKA KERJA UNTUK PENGKLUSTERAN ALIRAN TRAJEKTORI BERASASKAN TEKNIK PENGKLUSTERAN MIKRO TEMPORAL. PM. Oleh. Mac 2018. T. : Profesor Madya Norwati Mustapha, PhD : Sains Komputer dan Teknologi Maklumat. H. Pengerusi Fakulti. U. MUSAAB RIYADH ABDULRAZZAQ. ©. C. O. PY. R. IG. Dalam beberapa tahun ini, data spatio-temporal telah meningkat dengan pesat disebabkan oleh penyebaran luas peranti Geo-lokasi seperti Global Positioning System (GPS), telefon bimbit, sensor gerakan. Dalam sesetengah senario, data spatiotemporal diterima secara aliran. Pengklusteran data aliran adalah tugas mencabar disebabkan laluan tunggal terhadap data, saiz yang tiada had bagi data aliran, dan masa pemprosesan dan ruang ingatan yang terhad. Walau bagaimanapun, ianya adalah proses penting untuk banyak aplikasi seperti pengurusan trafik, kajian tingkah laku haiwan, dan ramalan cuaca. Banyak algoritma sedia ada untuk pengklusteran aliran bagi data trajektori mengeksploitasi teknik tetingkap masa yang mempartisi data aliran trajektori ke dalam bin-masa dan klusterkan segmen-segmen dalam setiap bin-masa secara berasingan. Memulakan pengklusteran dari awal dalam setiap bin-masa dan tidak mempertimbangkan hubungan antara objek daripada dua bin-masa berturutan mendorong kepada pembinaan kluster mikro bertindan yang memusat di kawasan sempadan antara dua bin-masa yang bersebelahan jika ianya adalah segmen trajektori yang sangat padat. Ini adalah kerana proses pengklusteran yang mengeksploitasi teknik tetingkap masa membina kluster mikro baharu untuk beberapa segmen trajektori pada permulaan setiap bin-masa walaupun segmen-segmen ini adalah sangat rapat (dalam jarak ambang) kepada kluster mikro pada hujung bin-masa sebelumnya. Ianya adalah benar iaitu kebanyakan mikro kluster yang paling dekat pada setiap binmasa yang berturutan boleh digabungkan bagi mengurangkan ruang ingatan tetapi pembinaan kluster mikro yang bertindan akan melambatkan tugas pengklusteran secara dramatik. Sebaliknya, pra-pemprosesan trajektori seperti segmentasi dan penapisan titik gangguan adalah langkah penting sebelum tugas melombong. Ini bertujuan mengurangkan saiz trajektori untuk meminimumkan masa pengklusteran. Kebanyakan algoritma sedia ada mengambil kira langkah penapisan gangguan sebelum langkah segmentasi trajektori yang bermaksud tiada kaedah prapemprosesan. iii.

(8) untuk mempartisikan trajektori ke dalam set segmen dan membuang titik gangguan dalam masa nyata dengan kos pengiraan yang rendah.. T. U. PM. Sebagai tindak balas terhadap kekurangan teknik tetingkap masa dan kaedah prapemprosesan secara luar talian sebagaimana yang telah diterangkan di atas, satu rangka kerja untuk pengklusteran aliran trajektori berasaskan jangka hayat kluster (SCT_TMC) dicadangkan. Rangka kerja ini terdiri dari dua peringkat: peringkat prapemprosesan dan peringkat pengklusteran aliran. Dalam peringkat prapemprosesan, Algoritma On-Line Noise Filtering untuk segmentasi trajektori berasaskan konsep Minimum Description Length (ONF_TRS) dicadangkan untuk mencapai segmentasi trajektori dan pembuangan titik gangguan dalam masa nyata dengan kos pengiraan yang rendah. Minimum Description Length adalah konsep penting dalam teori maklumat dan pembelajaran pengiraan yang merupakan hipotesis terbaik untuk set data yang diberi iaitu yang mendorong kepada pemampatan terbaik bagi data.. O. PY. R. IG. H. Peringkat pengklusteran aliran terdiri dari dua fasa: fasa dalam talian dan fasa luar talian. Dalam fasa dalam talian, algoritma pengklusteran aliran untuk trajektori berasaskan jangka hayat kluster dicadangkan (CC_TRS) untuk mengatasi kelemahan teknik tetingkap masa. Algoritma ini terdiri dari dua komponen: penjanaan mikro kluster temporal dan penggabungan kluster mikro temporal. Struktur data kluster mikro temporal dicadangkan dalam algoritma CC_TRS untuk menyimpan maklumat yang telah diringkaskan bagi setiap kumpulan segmen yang sama. Algoritma mengumpukkan masa hayat untuk setiap kluster mikro temporal yang baharu dibina supaya kumpulan segmen trajektori yang baru masuk akan berinteraksi hanya dengan kluster mikro temporal yang tidak tamat tempoh. Ini kerana kluster mikro temporal yang telah tamat tempoh menjadi jauh dari segmen semasa sama ada secara spatial atau secara temporal yang meminimumkan masa carian untuk kluster mikro temporal yang paling dekat. Apabila saiz kluster mikro temporal melebihi ruang ingatan yang diberikan, kluster mikro temporal yang paling dekat akan digabungkan untuk menjimatkan ingatan.. ©. C. Sebaliknya, fasa luar talian dipanggil apabila pengguna memohon untuk melihat keseluruhan hasil pengklusteran. Algoritma DBSCAN digunakan untuk melaksana tugas pengklusteran makro dengan menggantikan jarak antara segmen dengan jarak antara kluster mikro temporal. DBSCAN adalah pendekatan pengklusteran berasaskan ketumpatan yang sesuai untuk pengklusteran aliran disebabkan ciri-ciri; pertama, ia tidak memerlukan untuk menentukan bilangan kluster terlebih dahulu. Kedua, algoritma dapat mencari kluster yang pelbagai bentuk. Akhirnya, ia tahan terhadap gangguan dan unsur luaran. Cadangan stuktur data kluster mikro temporal dalam fasa dalam talian mendorong kepada penambahan fungsian bagi fasa pengklusteran makro. Fungsi yang telah dicadangkan untuk fasa luar talian ialah pengesanan unsur luaran spatial dan spatio-temporal dan pengklusteran makro.. iv.

(9) ©. C. O. PY. R. IG. H. T. U. PM. Analisis eksperimen yang komprehensif telah dijalankan untuk menilai kecekapan dan keberkesanan algoritma yang dicadangkan (ONF-TRS, CC-TRS). Hasil menunjukkan ONF-TRS mempunyai kos pengiraan yang rendah dan kadar mampatan yang tinggi berbanding dengan kerja sedia ada. Sementara itu, CC-TRS menambahbaik kelajuan tugas pengklusteran berbanding dengan hasil kerja terkini.. v.

(10) ACKNOWLEDGEMENTS. PM. First, I would like to express my sincere gratitude to my supervisor Associate Professor Dr. Norwati Mustapha for giving me an opportunity to start this study. Through the courses of my study, I have had the great fortune to get to know and interact with her. Her comments and suggestions for further development as well as her assistance during writing this thesis are invaluable to me.. U. I would like to express my sincere thanks and appreciation to the supervisory committee members Associate Professor Dr. Md Nasir Sulaiman and Associate Professor Dr. Nurfadhlina Mohd Sharef for their guidance, valuable suggestions and advice throughout this work in making this a success.. H. T. My deepest appreciation to my wife Ms. Zainab and my daughter Layan who have been supportive and patiently waiting for me to complete my study. Finally, I owe my sincere thanks to my parents, brother, and sisters for their encouragement and affirmation, which made it possible for me to achieve this work.. ©. C. O. PY. R. IG. For the others who have directly or indirectly helped me in the completion of my work, I thank you all.. vi.

(11) ©. T. H. IG. R. PY. O. C. PM. U.

(12) Norwati Mustapha, PhD Associate Professor Faculty of Computer Science and Information Technology Universiti Putra Malaysia (Chairman). T. U. MD. Nasir B Sulaiman, PhD Associate Professor Faculty of Computer Science and Information Technology Universiti Putra Malaysia (Member). PM. This thesis was submitted to the Senate of the Universiti Putra Malaysia and has been accepted as fulfillment of the requirement for the degree of Doctor of Philosophy. The members of the Supervisory Committee were as follows:. PY. R. IG. H. Nurfadhlina Mohd Sharef, PhD Associate Professor Faculty of Computer Science and Information Technology Universiti Putra Malaysia (Member). C. O. ROBIAH BINTI YUNUS, PhD Professor and Dean School of Graduate Studies Universiti Putra Malaysia. ©. Date:. viii.

(13) Declaration by graduate student. Date:. R. Signature:. IG. H. T. U. PM. I hereby confirm that:  this thesis is my original work;  quotations, illustrations and citations have been duly referenced;  this thesis has not been submitted previously or concurrently for any other degree at any institutions;  intellectual property from the thesis and copyright of thesis are fully-owned by Universiti Putra Malaysia, as according to the Universiti Putra Malaysia (Research) Rules 2012;  written permission must be obtained from supervisor and the office of Deputy Vice-Chancellor (Research and innovation) before thesis is published (in the form of written, printed or in electronic form) including books, journals, modules, proceedings, popular writings, seminar papers, manuscripts, posters, reports, lecture notes, learning modules or any other materials as stated in the Universiti Putra Malaysia (Research) Rules 2012;  there is no plagiarism or data falsification/fabrication in the thesis, and scholarly integrity is upheld as according to the Universiti Putra Malaysia (Graduate Studies) Rules 2003 (Revision 2012-2013) and the Universiti Putra Malaysia (Research) Rules 2012. The thesis has undergone plagiarism detection software. ©. C. O. PY. Name and Matric No: Musaab Riyadh Abdulrazzaq, GS38799. ix.

(14) Declaration by Members of Supervisory Committee. Associate Professor Dr. MD. Nasir B Sulaiman. Signature: Name of Member of Supervisory Committee:. Associate Professor Dr. Nurfadhlina Mohd Sharef. ©. C. O. PY. R. IG. H. T. Signature: Name of Member of Supervisory Committee:. U. Signature: Name of Chairman of Supervisory Associate Professor Committee: Dr. Norwati Mustapha. PM. This is to confirm that: the research conducted and the writing of this thesis was under our supervision;   supervision responsibilities as stated in the Universiti Putra Malaysia (Graduate Studies) Rules 2003 (Revision 2012-2013) were adhered to.. x.

(15) TABLE OF CONTENTS. Page i iii vi vii ix xiv xv xix. U. PM. ABSTRACT ABSTRAK ACKNOWLEDGEMENTS APPROVAL DECLARATION LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS CHAPTER INTRODUCTION 1.1 Background 1.2 Problem statement 1.3 Research objectives 1.4 Research scope 5.1 Research contributions 1.6 Organization of the thesis. 2. LITERATURE REVIEW 2.1 Introduction 2.2 Background 2.3 Challenges and Issues in Trajectory Streams Clustering Trajectory data preprocessing 2.4 2.4.1 Data cleaning 2.4.2 Noise filtering 2.4.3 Stay point detection 2.4.4 Trajectory compression 2.4.5 Trajectory segmentation 2.4.6 Map matching 2.5 Distance/ Similarity measure of trajectories 2.5.1 Global distance measures 2.5.2 Local distance measures 2.5.2.1 Minimum Bounding Rectangles (MBR) 2.5.2.2 Trajectory Hausdorff Distance 2.5.2.3 Structural SIMilarity Measure (SSIM) 2.5.2.4 Trajectory segment distance 2.5.2.5 The tightened distance 2.6 Trajectories clustering 2.6.1 Static clustering of trajectories 2.6.1.1 Model based clustering 2.6.1.2 Partition and group based clustering. ©. C. O. PY. R. IG. H. T. 1. xi. 1 1 2 4 4 5 5. 7 7 7 10 11 11 12 13 14 15 16 17 17 20 20 21 22 22 23 23 25 26 26.

(16) RESEARCH METHODOLOGY 3.1 Introduction 3.2 Research steps 3.3 Experimental Design 3.3.1 Data sets 3.3.1.1 Starkey project data set 3.3.1.2 Atlantic Hurricane data set 3.3.2 Performance metrics 3.3.2.1 ONF_TRS algorithm evaluation 3.3.2.2 The Evaluation of Temporal Micro Clustering Technique 3.3.2.3 The overall clustering evaluation 3.4 System requirements (Software and Hardware) 5.1 Summary. 49 50 51 51. STREAM CLUSTERING OF TRAJECTORIES BASED ON TEMPORAL MICRO CLUSTERING TECHNIQUE (SCT-TMC) 4.1 Introduction 4.2 Pre-processing stage 4.2.1 Pre-treatment component 4.2.2 Pre-processing component 4.2.2.1 Trajectory Noise Points Filtering 4.2.2.2 Trajectory Segmentation 4.2.2.3 Minimum Description Length 4.2.2.4 ONF-TRS algorithm 4.3 The Stream Clustering Stage 4.3.1 The Online phase 4.3.1.1 The limitations of time window technique 4.3.1.2 Temporal micro cluster generation 4.3.1.3 CC-TRS algorithm. 52 52 53 54 54 54 55 56 58 63 63 63 64 69. ©. C. O. 4. PY. R. IG. H. T. 3. 27 27 28 29 30 30 30 32 32 36 36 36 37 37 38. PM. 2.8 2.9. U. 2.7. 2.6.1.3 Place of interest based clustering 2.6.1.4 Moving together pattern 2.6.1.5 Uncertain trajectory clustering 2.6.1.6 Semantic based clustering 2.6.1.7 Optimization strategies 2.6.2 Stream clustering of trajectories 2.6.2.1 Window Models 2.6.2.2 Object based model 2.6.2.3 Stream clustering algorithms Clustering Validation 2.7.1 Overall similarity 2.7.2 Precision and recall 2.7.3 Internal measures The importance of trajectory data stream clustering application Summary. xii. 39 39 39 43 43 45 46 47 48.

(17) 4.4. 4.3.1.4 Temporal Micro Clustering Merging 4.3.2 The Offline phase 4.3.2.1 The DBSCAN algorithm 4.3.2.2 Spatial Outliers detection 4.3.2.3 Spatio-temporal Outliers detection Summary. 72 76 77 78 80 80. RESULTS AND DISCUSSION 81 5.1 Introduction 81 5.2 ONF-TRS algorithm evaluation (pre-processing stage) 81 5.2.1 Compression rate 81 5.2.2 Computational Complexity 87 5.2.3 Purity and coverage 87 5.3 Stream clustering evaluation 88 5.3.1 The online clustering evaluation 88 5.3.1.1 Temporal Micro Clustering Technique vs Time Window Technique 88 5.3.1.2 Parameter Φ effect 95 5.3.1.3 Parameter dmax effect 95 5.3.2 Macro clustering (the offline phase) 96 1.5.3.5 Clustering quality evaluation 96 1.5.3.3 Running time evaluation 98 5.4 Summary 100. 6. CONCLUSIONS AND FUTURE WORK 6.1 Conclusions 6.2 Future work. PY. R. IG. H. T. U. PM. 5. 103 114 115. ©. C. O. REFERENCES BIODATA OF STUDENT LIST OF PUBLICATIONS. 101 101 102. xiii.

(18) LIST OF TABLES. Table. Page Time series distance functions for two trajectories of length m, n. 18. 2.2. Traditional and Streaming processing. 24. 2.3. Well-Known Methods in Stream Clustering of Trajectories with their main features. 35. 3.1. The main variables of the Starkey project. 46. 3.2. Number of trajectories for each specie for Starkey data. 46. 4.1. The (region/ length) threshold value for different triangle base length. 62. 5.1. The compression ratio of MDL, GRASP-UTS, and for Elk 93. ONF-TRS. 84. 5.2. The compression ratio of MDL, GRASP-UTS, and using Deer 95 data set. ONF-TRS. 86. 5.3. Evaluate the generated segments from ONF-TRS, MDL, GRASPUTS algorithms based on the average purity and coverage. 87. ©. C. O. PY. R. IG. H. T. U. PM. 2.1. xiv.

(19) LIST OF FIGURES. Figure. Page Time windows technique for k trajectories. 2. 2.1. Classes of Spatio-temporal data. 8. 2.2. Noise Points in Trajectory. 2.3. Stay Points in a Trajectory. 2.4. A schematic of the trajectory compression. 2.5. Similarity measure based on the overlapping between MMBs. 19. 2.6. Area based similarity measure. 19. 2.7. MBR distance between trajectories segments. 2.8. Trajectory-Hausdorff distance. 2.9. Trajectory segment distance a) segment distance b) the tightened distance. 23. 2.10. Two semantic trajectories A&B. 29. 2.11. Sliding windows model. 31. PM. 1.1. 12 13. PY. R. IG. H. T. U. 14. 22. Damped windows model. 31. 2.13. Landmark window model. 31. O. 2.12. 2.14. object-based data stream clustering framework. 32. 3.1. Research methodology steps. 42. 3.2. Trajectory points Elk 93 data set. 44. 3.3. Starkey project data for two species of animal Elk 93 and Deer 95. 45. 3.4. Format of Hurricane trajectories data sets. 46. 3.5. Tropical data for Atlantic Hurricane year 2000 & 2010. 47. 4.1. The proposed Framework SCT-TMC. 53. C. ©. 20. xv.

(20) Trajectory Characteristics Points. 55. 4.3. Formulation of the MDL cost. 56. 4.4. MDLnpar and MDLpar for each points in trajectory. 57. 4.5. Collinear Points (region/length) = 0. 58. 4.6. Non-significant point small (region/length). 58. 4.7. Noisy point, (region/length) is small. 4.8. Significant point High value of (region/length). 4.9. The ONF-TRS algorithm steps. 4.10. The value of H such that MDLpar=MDLnopar. 4.11. Algorithm for estimation of (region/ length) threshold for three points. 4.12. Time windows technique for k trajectories. 4.13. The representative line of TMC. 66. 4.14. Three components of the distance function (dcen, dθ, and d‖). 67. 4.15. Temporal Micro Cluster Extent. 68. 4.16. Extent value (loose vs tight TMC). 69. 4.17. The temporal micro clustering technique. 70. 4.18. The CC_TRS algorithm. 71. O. PY. R. IG. H. T. U. 59. ©. 59 60 61 62. 64. Merging tight temporal micro-clusters. 72. 4.20. Merging loose temporal micro clusters. 73. 4.21. Distance with extent for tight and loose TMC. 73. 4.22. The center distance with extend (δ). 74. 4.23. The angle distance with extend (δ). 75. 4.24. The parallel distance with extend (δ). 75. 4.25. Temporal micro cluster merging. 76. C. 4.19. PM. 4.2. xvi.

(21) The Entire stream clustering stage (online and offline phases). 77. 4.27. The output of DBSCAN clustering algorithm. 78. 4.28. The common moving trend shared by various objects for Elk 93 data set. 78. 4.29. The common moving trend shared by various objects of Deer 95 data set. 79. 4.30. The plotting of the Dear 95 data set. 79. 4.31. The spatial outlier of Deer 95 data set. 5.1. The actual size and the compressed size of MDL, GRASP-UTS and ONF-TRS for Elk 93.. 82. 5.2. Trajectory noise point in Elk 93 data set. 83. 5.3. The actual size and the compressed size of MDL, GRASP-UTS, and ONF-TRS for Deer 95.. 85. 5.4. Trajectory noise point in Deer 95 data set. 85. 5.5. The number of MCs which created by TWT and TMCT for Elk 93 data set. 89. 5.6. The clustering quality (SSQ) for TWT and TMCT for Elk 93 data set. PM. 4.26. PY. R. IG. H. T. U. 80. The number of MCs which created by TWT and TMCT for Deer 95 data set. 90. 5.8. The clustering quality (SSQ) for TWT and TMCT for Deer 95 data set. 90. The number of MCs which created by TWT and TMCT for Hurricane data set. 90. 5.10. The clustering quality (SSQ) for TWT and TMCT for Hurricane data set. 91. 5.11. The number of MCs which created by TWT and TMCT for Elk 93 data set. 91. 5.12. The Clustering Quality (SSQ) For TWT and TMCT for Elk 93 data Set. 92. O. 5.7. C. 5.9. ©. 89. xvii.

(22) The Number Of MCs Which Created By TWT And TMCT for Deer 95 Data Set. 92. 5.14. The clustering quality (SSQ) for TWT and TMCT for Dear 95 data set. 92. 5.15. The number of MCs which created by TWT and TMCT for Hurricane data set. 93. 5.16. The clustering quality (SSQ) for TWT and TMCT for Hurricane data set. 93. 5.17. The running time of TMCT and TWT for of Elk 93 data set. 94. 5.18. The running time of TMCT and TWT for Deer 95 data set. 94. 5.19. The running Time of TMCT and TWT for Atlantic Hurricane data set. 94. 5.20. The minimum running time for Elk 93, Deer 95, and Atlantic Hurricane based on different value of transfer time Φ. 95. 5.21. The sensitivity of parameter dmax on clustering quality (SSQ). 96. 5.22. The sensitivity of parameter dmax on running time. 96. 5.23. The Clustering Quality (SSQ) for SCT-TMC, TCMM, ConTraClu, and CUTiS On Elk 93 Data Set. 97. 5.24. The Clustering Quality (SSQ) For SCT-TMC, ConTraClu, and CUTiS On Deer 95 Data Set. TCMM,. 97. 5.25. The clustering quality (SSQ) For SCT-TMC, TCMM, ConTraClu, and CUTiS On Hurricane Data Set. 98. O. PY. R. IG. H. T. U. PM. 5.13. The running Time For SCT-TMC, TCMM, ConTraClu, and CUTiS on Elk 93 Data Set. 98. 5.27. The running Time For SCT-TMC, TCMM, ConTraClu, and CUTiS on Deer 95 Data Set. 99. 5.28. The Running Time For SCT-TMC, TCMM, ConTraClu, and CUTiS on Atlantic Hurricane Data Set. 99. ©. C. 5.26. xviii.

(23) LIST OF ABBREVIATIONS. Compression Ratio. MBB. Minimal Bounding Boxes. MBR. Minimum Bounding Rectangle. MC. Micro Cluster. MDL. Minimum Description Length. POI. Points of interest. SSQ. Sum of SQuare distance. TD. Trajectory Data. TMC. Temporal Micro Cluster. TMPT. Temporal Micro Clustering Technique. U. T. H. IG. Time Window Technique. ©. C. O. PY. R. TWT. PM. CR. xix.

(24) CHAPTER 1 1 1.1. INTRODUCTION. Background. IG. H. T. U. PM. The rapid advances in hardware technology such as Global Position Systems (GPS) devices, radio-frequency identification (RFID) readers, mobiles phones, and navigational systems lead to generate huge amount of online trajectories data for different kind of free moving objects such as people, natural phenomena, and animals (Zheng et al. 2015; Pelekis et al. 2017). These kind of online trajectories data are referred to as trajectory stream data. The stream clustering of trajectory data aims to extract useful knowledge regarding different applications such as locating the heavy traffic paths in road networks, weather forecasting and monitoring the migration behavior of animals. However, many challenges can be arisen from stream clustering of trajectory data: Firstly, the massive size of stream data and limited system memory. Secondly, constantly arriving data and limited time for processing; therefore, it is not feasible to process the data efficiently using numerous passes. Finally, sensors data subject to noise, missing, errors, and incomplete readings because of sensors inaccuracy (Aggarwal et al. 2013; Galić et al. 2016). Therefore, traditional clustering approaches must be redeveloped to mitigate the difficulties associated with stream data clustering.. ©. C. O. PY. R. The challenges of trajectory stream clustering have captured the attention of many researchers, e.g. ( Li et al. 2004, Elnekave et al 2007; Jensen et al. 2007; Zhou at el 2008; Li et al. 2010; Yu et al. 2013a; Yu et al. 2013b; Gianni et al. 2014; Costa et al. 2014; Jin et al. 2014; Da Silva et al. 2016a; Da Silva et al 2016b; Da Silva et al 2016c; J. Mao et al. 2016). Most of the researches conducted to date have relied on the object based model, this model consists of two phases: the online phase (also called microclustering) and the offline phase (macro clustering). In the online phases, a data structure named micro-clusters MC is used to keep summarized information for each similar group of sub-trajectories since the entire stream data cannot be stored in the memory. While in the macro clustering phase, any density based approach can be used to group MCs instead of the raw data. On the other hand, finding the similarity between pair of trajectories is the essence of clustering task. There are two approaches to find the distance between two trajectories: the global distance approach and local distance approach. The global distance approach considers the entire trajectory as one unit into similarity computation. This approach requires significant memory storage and processing time. Moreover, the local patterns in a certain area are rarely located. The local distance approach aims to compute the similarity between two subtrajectories. Lee et al. 2007 suggests a novel method based on the minimum description length to partition trajectories into set of sub-trajectories and compute the similarity between them. Finally, Data pre-processing such as segmentation and noise filtering is an important step precedes the mining task. Various segmentation algorithms has been suggested to partition trajectory into set of segment, some of these 1.

(25) algorithms have a high computation cost such as (Panagiotakis et al. 2012; Buchin et al. 2013; Alewijnse et al. 2014), others are noise sensitive methods such as (Soares et al. 2015). The pre-processing step aims to reduce the size of the trajectory and enhanced its quality to enhance the efficiency of the clustering algorithm.. Problem statement. T. 1.2. U. PM. In this study, a framework for stream clustering of trajectories based on temporal micro clustering technique is proposed, the framework consists of two stages: preprocessing and stream clustering stage. In pre-processing stage, a new algorithm has been proposed base on minimum description length to achieved trajectory segmentation and noise points filtering in real time with low computational cost. In stream clustering stage, the temporal micro clustering technique has been proposed to improve the clustering quality and minimize computational cost of clustering in online and offline phases.. O. PY. R. IG. H. Clustering of trajectory stream data requires algorithms which can quickly update clustering results for the continually arriving data. Commonly, processing of stream data which rely on a time window model (e.g. Li et al. 2010; Yu et al. 2013a; Yu et al. 2013b; Da Silva et al 2016c) disjoints the stream data into portions (time-bins) based on time or on the number of processed items and perform the clustering task at each time-bins separately without taking into the consideration the relationships among the objects in those consecutive time-bins. Initiating clustering from scratch in each timebin leads to the disturbance in the clustering task which centralizes in the border area between two adjacent time-bins especially if it is very dense of trajectory segments. This is because the clustering process applying the time window technique creates redundant micro-clusters MCn for some trajectory segments Si at the beginning of a certain time-bink+2 even though these segments are very close (within distance threshold) to the micro-clusters MCm at the end of the preceding time-bink+1 as illustrated in Figure 1-1.. Y. Time. MCm. ©. C. Tr1. Tr2. Trk Time-bink. MCn Time-bink+1. Figure 1.1 : Time window technique for k trajectories 2. Time-bink+2. X.

(26) It is true that the similar micro-clusters of MCn and MCm can be grouped during the merging phase when the MCs size exceed a given memory space, but creating redundant micro-clusters will significantly affect the efficiency and effectiveness of clustering algorithms in the online phase and offline phase for four reasons:. IG. H. T. U. PM. 1. The memory size which occupied by redundant MCs will quickly magnify the overall size of the MCs which lead to minimizing the time period between two consecutive merging operations. Merging is an extremely time consuming task since it finds the similarity between all MCs to merge the most similar ones. 2. The additional number of redundant MCs will significantly maximize the running time of the merging task. This is because the computational complexity of merging task for TCMM framework (Li et al. 2010) is O(n!) where n represents the number of MCs. 3. Any clustering approach aims to find an optimal trade-off between two properties: accuracy and conciseness. Accuracy means the sum of all distances between the cluster centers and their members must be as small as possible; while conciseness means the number of clusters should be as small as possible. The redundant number of MCs will outweigh accuracy over conciseness. Therefore, the clustering quality will be degraded and the clustering task will be time consuming. 4. In the offline phase, the DBSCAN algorithm is used to deliver the overall clustering results, the computational cost of DBSCAN is O(n log(n)) where n denotes the number of MCs, therefore redundant MCs will also increase the running time of the offline phase.. ©. C. O. PY. R. On the other hand, the trajectory pre-processing task such as segmentation and noise points filtering is a crucial step prior to clustering step. It aims to reduce the size of the trajectory and improve its quality. Lee et al. 2007; Li et al. 2010 proposed methods based on the minimum description length principle to partition the trajectory into set of characteristic points where the behavior of a trajectory changes rapidly. Two problems can be addressed with these methods: firstly, they needs at least three points in advance to decide whether the trajectory points are characteristic or not. Secondly, these methods are significantly affected by noisy data. Soares et al. 2015 suggested GRASP-UTS algorithm based on minimum description length to partition trajectory into a set of land-marks. The algorithm modifies the land-mark locations (i.e. deleting, inserting, and changing) to gain maximum homogeneity. The GRASP-UTS is an iterative algorithm and noise sensitive. Buchin et al 2011, Buchin et al 2013 specified a set of spatial and temporal features such as shape, speed, and direction to partition trajectories into minimum number of segments. Theses algorithms are inconvenient to pre-processing data in real time due to their high computational cost O(n log n). Thus, as far as our present knowledge in this area, there is no pre-processing method currently available to divide trajectory into set of segments and robust to noise with low computational cost.. 3.

(27) 1.3. Research objectives. The main objective of this study is to propose an efficient and effective framework for stream clustering of trajectories based on temporal micro cluster technique. To achieve the objective, the following ideas are adopted: . PM. To propose pre-processing algorithm based on minimum description length concept to divide trajectory into set of characteristics point and filtering noise points in real time. The algorithm must require minimum number of points in advance and has low computational cost. To propose the temporal micro clustering technique in online phase which assigns a life time for each newly created micro cluster. The new batch of segments will interact only with the non-expired micro clusters which lead to reduce the running time in online phase since the most time-consuming part is finding the nearest micro-cluster. To propose new data structure which supports the spatio-temporal merging of two micro clusters in the online phase and to expand the functionality of the offline phase.. U. . Research scope. IG. 1.4. H. T. . This study is dedicated for free space trajectories where objects do not follow a path within a road network. This is because, free space trajectories has wide spectrum of crucial applications such as extract the migratory trajectories of animals, weather forecasting, and detection of suspicious behaviors in trajectories of people for security issue The similarity between sub trajectory pair is based on their geometric properties (length, center, and orientation) due to the complexity of free moving object trajectory. Any semantic information such as semantic labels are not taken into the consideration in this study since they cannot be generated in real time. The discovery of groups of trajectories that move close to each other for a period of time such as flock and swarm is out of scope of the study since it needs to monitor the individual trajectories separately. The stream clustering algorithm assigns the same weight for both recent and historical data which means no time fading models is considered.. O. . PY. R. The scope of this study is focused on mining knowledge from the unlabeled stream data of trajectories without any previous knowledge about the data. Therefore, an unsupervised learning method such as clustering must be applied. It is important to outline some of the critical assumptions concerning the proposed framework:. ©. C. . . . 4.

(28) Collecting the data is the first priority of trajectories data mining. There are a few real trajectories data sets that are available for free moving objects of trajectories such as GeoLife Trajectory Dataset which comprises Elk 93 (33 trajectory), and Dear 95 (32 trajectories), and Atlantic Hurricane trajectories (1740 trajectories). These data set has been adopted in this study due to their high sampling rate (high speed generation). Therefore, the data sets is convenient for stream clustering. Research contributions. PM. 1.5. U. The main contribution of this study is to devising an efficient framework for stream clustering of trajectories data. The framework aims to reduce running time and improve the clustering quality compared with existing works which exploit time window technique. The novel characteristics of the proposed framework in preprocessing and stream clustering stages are as follow:. PY. R. IG. H. T. 1. The ONF-TR algorithm which is based on the minimum description length concept to partition trajectory into set of segments and filtering noise points. The main characteristics of the ONF_TR algorithm are: requires only three points in advance and has low computational cost O(n) where n is the number of points in trajectory. These characteristics highly met the requirements of stream data clustering. 2. The “temporal micro clustering” technique in the online phase. This technique minimizes the number of micro clusters which are created by time window technique. This leads to enhance the efficiency of clustering algorithm CC-TRS and improve its clustering quality. 3. The temporal micro cluster data structure which supports the spatio-temporal dimensions when merging two temporal micro cluster. Besides that, the data structure aids to detect the spatial outliers and spatio-temporal outliers efficiently in the offline phase.. Organization of the thesis. O. 1.6. ©. C. The thesis is formatted according to the standard structure of thesis and dissertation of universiti Putra Malaysia. Chapter 2 presents background knowledge about trajectories which include trajectory stream mining and its challenging, trajectories preprocessing techniques and methods, and trajectories similarity. Besides that, a literature review of three areas related to the concerns of this study is done which comprise: trajectory data clustering, the traditional data stream clustering and trajectory stream clustering. Chapter 3 covers the methodology steps which are followed in this study. Also, it highlights the proposed framework, the data sets and performance metrics which are used to evaluate framework algorithms, and how the proposed algorithms will be assessed. Chapter 4 is dedicated for the suggested framework for trajectories stream clustering and elaborately explains the new algorithms and Models which are proposed for the different stages of the proposed framework. Chapter 5 highlights the experimental results which got from comparing 5.

(29) ©. C. O. PY. R. IG. H. T. U. PM. of the proposed algorithms and the major latest works related to stream clustering of trajectories. Chapter 6 concludes the thesis by summarizing the findings and shed light on the open issues which can be followed in the future studies.. 6.

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