distributed clustering algorithm

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ENERGY EFFICIENT HIERARCHICAL AND DISTRIBUTED CLUSTERING ALGORITHM FOR EFFICIENT FORMATION OF CLUSTERS IN WIRELESS SENSOR NETWORK

ENERGY EFFICIENT HIERARCHICAL AND DISTRIBUTED CLUSTERING ALGORITHM FOR EFFICIENT FORMATION OF CLUSTERS IN WIRELESS SENSOR NETWORK

Wireless sensor networks are network of sensor nodes with a set of processors and small memory unit embedded in it. Unfailing routing of packets from sensor nodes to its base station is the most significant function for these networks. The conservative routing protocols cannot be applied here due to its battery powered nodes. To provision energy efficiency, nodes are frequently clustered in to non-overlapping clusters. A distributed clustering methodology, the energy efficient hierarchical distributed clustering algorithm (EHDCA) has been proposed. It is a well-distributed clustering mechanism and the cluster head selection is based on residual energy, communication cost and the distance to the base station. The main characteristic feature of the proposed methodology is the cluster head selection is carried out in just few steps. The performances of the proposed clustering methodology have been compared with LEACH. Its hierarchical nature shall be effectively employed for reduction in total energy consumption and backbone energy consumption. The energy efficiency and overall network lifetime shall be greatly improved.
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Enhanced Uniform Distributed Clustering Algorithm (UDCA) In Wireless Sensor Network

Enhanced Uniform Distributed Clustering Algorithm (UDCA) In Wireless Sensor Network

Many literatures are concentrated on energy efficiency in wireless sensor network. Various clustering algorithms are going on. But we are working on uniform distributed clustering algorithm (UDCA). Overall performance of UDCA is good. But it has some limitations in terms of energy efficiency. Basically UDCA work in three rounds. In very first round energy consumption is very high. So ,proposed work is focus to reduce the communication energy and time between BS and CHs. We have done some changes into the first step of UDCA. Initially, there was a broadcast communication between the base station and nodes. But into this modified algorithm, we are using multicast communication between the base station and nodes. In first round of UDCA, base station sends cluster head message to all nodes by broadcast medium, but it is energy consuming process. Because, all member nodes will check that message to match their node ids. But in enhanced UDCA we are using multicast medium. Base station will send cluster head id message to that particular nodes, which are selected as cluster heads. Now all member nodes will not check CH-ID message and their energy will be conserved.
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An energy efficient distributed clustering algorithm for heterogeneous WSNs

An energy efficient distributed clustering algorithm for heterogeneous WSNs

Wireless sensor networks (WSNs) were envisaged to become the fabric of our environment and society. However, they are yet unable to surmount many operational challenges such as limited network lifetime, which strangle their widespread deployment. To prolong WSN lifetime, most of the existing clustering schemes are geared towards homogeneous WSN. This paper presents enhanced developed distributed energy-efficient clustering (EDDEEC) scheme for heterogeneous WSN. EDDEEC mainly consists of three constituents i.e., heterogeneous network model, energy consumption model, and clustering-based routing mechanism. Our heterogeneous network model is based on three energy levels of nodes. Unlike most works, our energy consumption model takes into account the impact of radio environment. Finally, the proposed clustering mechanism of EDDEEC changes the cluster head selection probability in an efficient and dynamic manner. Simulation results validate and confirm the performance supremacy of EDDEEC compared to existing schemes in terms of various metrics such as network life.
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A Research on Smart Transportation using Sensors and Embedded Systems

A Research on Smart Transportation using Sensors and Embedded Systems

and Sophia S., ‘Hierarchical distributed clustering algorithm for energy efficient wireless sensor networks’, International Journal of Research in Information Technology, 1(12), 2013.[r]

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Vol 8, No 1 (2013)

Vol 8, No 1 (2013)

Latest researches in wireless communications and electronics has imposed the progress of low-cost wireless sensor nodes. Clustering is a thriving topology control approach, which can prolong the lifetime and increase scalability for wireless sensor networks. The admired criteria for clustering methodology are to select cluster heads with more residual energy and to rotate them periodically. Sensors at heavy traffic locations quickly deplete their energy resources and die much earlier, leaving behind energy hole and network partition. In this paper, a model of distributed layer-based clustering algorithm is proposed based on three concepts. First, the aggregated data is forwarded from cluster head to the base station through cluster head of the next higher layer with shortest distance between the cluster heads. Second, cluster head is elected based on the clustering factor, which is the combination of residual energy and the number of neighbors of a particular node within a cluster. Third, each cluster has a crisis hindrance node, which does the function of cluster head when the cluster head fails to carry out its work in some critical conditions. The key aim of the proposed algorithm is to accomplish energy efficiency and to prolong the network lifetime. The proposed distributed clustering algorithm is contrasted with the existing clustering algorithm LEACH.
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Distributed K-Modes Clustering in P2P Networks

Distributed K-Modes Clustering in P2P Networks

In this section, empirical evidence is provided for D-K- Md algorithm that the high quality globalcluster models is obtained with limited communication overhead and high level of privacy. The efficiency of DK-Md is compared with existing distributed clustering algorithm, DKM along with CC, where all local datasetsare merged and clustered using K-Modes algorithm. The existing DKM algorithm is not directlyendurable for categorical datasets, because it uses the local clustering algorithm as K-Means and Euclidean distance for the computation of local and global centroid. To execute this algorithm for categorical datasets, thevalues of each attribute are converted into number format by assigning sequential numbers for each category.For example, if an attribute „color‟ contains three values such as „blue‟, „green‟, and „red‟, they are mapped tothree sequential numbers such as 1, 2, and 3.
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DENCAST: distributed density-based clustering for multi-target regression

DENCAST: distributed density-based clustering for multi-target regression

Recent developments in sensor networks and mobile computing led to a huge increase in data generated that need to be processed and analyzed efficiently. In this context, many distributed data mining algorithms have recently been proposed. Following this line of research, we propose the DENCAST system, a novel distributed algorithm implemented in Apache Spark, which performs density-based clustering and exploits the identified clusters to solve both single- and multi-target regres- sion tasks (and thus, solves complex tasks such as time series prediction). Contrary to existing distributed methods, DENCAST does not require a final merging step (usually performed on a single machine) and is able to handle large-scale, high-dimensional data by taking advantage of locality sensitive hashing. Experiments show that DEN- CAST performs clustering more efficiently than a state-of-the-art distributed clustering algorithm, especially when the number of objects increases significantly. The quality of the extracted clusters is confirmed by the predictive capabilities of DENCAST on several datasets: It is able to significantly outperform (p-value < 0.05 ) state-of-the-art distrib- uted regression methods, in both single and multi-target settings.
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BIOMEDICAL APPLICATIONS OF LARGE SCALE WIRELESS NETWORKS Dr. E. Gajendran*, Dr. J. Vignesh** & Dr. S. R. Boselin Prabhu***

BIOMEDICAL APPLICATIONS OF LARGE SCALE WIRELESS NETWORKS Dr. E. Gajendran*, Dr. J. Vignesh** & Dr. S. R. Boselin Prabhu***

and Sophia S., „Hierarchical distributed clustering algorithm for energy efficient wireless sensor networks‟, International Journal of Research in Information Technology, 1(12), 2013. B[r]

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Vol 5, No 1 (2013)

Vol 5, No 1 (2013)

size and those farther from CH are smaller in size. It is proved to be energy efficient in intra-cluster communication and excellent improvement in the total network lifetime. Energy Efficient Unequal Clustering mechanism (EEUC), was anticipated for uniform energy consumption within the network. It forms unequal clusters, with an supposition that each cluster can have variable sizes. Based on nodes’ residual energy, connectivity and a unique node identifier, the cluster head selection is done in Distributed Efficient Clustering Approach (DECA). It is extremely energy efficient, as it uses fewer messages for CH selection. The main problem with this algorithm is that high possibility of incorrect CH selection which leads to discarding of all the packets sent by the sensor node. In order to select CH based on weight: a blend of nodes’ residual energy and its distance to neighboring nodes, Distributed Weight-based Energy-efficient Hierarchical Clustering (DWEHC) has been proposed [19-22]. It generates well balanced clusters, independent on network topology or dimension. Hybrid Energy- Efficient Distributed Clustering (HEED) [2] is a well distributed clustering algorithm in which CH selection is made by taking into account the residual energy of the nodes as well as intra-cluster communication cost leading to prolonged network lifetime.
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An Enhanced Distributed Weighted Clustering Algorithm for Intra and Inter Cluster Routing in MANET

An Enhanced Distributed Weighted Clustering Algorithm for Intra and Inter Cluster Routing in MANET

 Wojciech Bednarczyk,Piotr Gajewskil [3] proposed An enhanced Algorithm for MANET clustering based on Weighted parameters, in this approach CH election considers Degree of the node, received power level, stationary factor and remaining battery level. This algorithm is applicable for different scenarios by changing the weight factors.  Sahar Adabi et al,[4] proposed "A Novel Distributed Clustering Algorithm for Mobile Adhoc Network",in this(DSBCA) each node calculate its score by linear algorithm, based on battery remaining, number of neighbours, number of members and stability. The node's neighbors are notified about the score value. The scores compared and the node with highest score elected as CH.
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AN INVESTIGATION ON MONITORING CARDIAC ACTIVITIES USING MICROCONTROLLER

AN INVESTIGATION ON MONITORING CARDIAC ACTIVITIES USING MICROCONTROLLER

and Sophia S., „Hierarchical distributed clustering algorithm for energy efficient wireless sensor networks‟, International Journal of Research in Information Technology, 1(12[r]

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Ensemble based Distributed K-Modes Clustering

Ensemble based Distributed K-Modes Clustering

Abstract:- Clustering has been recognized as the unsupervised classification of data items into groups. Due to the explosion in the number of autonomous data sources, there is an emergent need for effective approaches in distributed clustering. The distributed clustering algorithm is used to cluster the distributed datasets without gathering all the data in a single site. The K-Means is a popular clustering method owing to its simplicity and speed in clustering large datasets. But it fails to handle directly the datasets with categorical attributes which are generally occurred in real life datasets. Huang proposed the K-Modes clustering algorithm by introducing a new dissimilarity measure to cluster categorical data. This algorithm replaces means of clusters with a frequency based method which updates modes in the clustering process to minimize the cost function. Most of the distributed clustering algorithms found in the literature seek to cluster numerical data. In this paper, a novel Ensemble based Distributed K-Modes clustering algorithm is proposed, which is well suited to handle categorical data sets as well as to perform distributed clustering process in an asynchronous manner. The performance of the proposed algorithm is compared with the existing distributed K-Means clustering algorithms, and K-Modes based Centralized Clustering algorithm. The experiments are carried out for various datasets of UCI machine learning data repository.
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Comparative Study of Weighted Clustering Algorithms for Mobile Ad Hoc Networks

Comparative Study of Weighted Clustering Algorithms for Mobile Ad Hoc Networks

The weight-based distributed clustering algorithm (WCA) [4] takes into consideration, the ideal degree, transmission power, mobility, and battery power of mobile nodes. Depending on specific applications, any or all of these parameters can be used in the metric to elect the CHs. This is based on fully distributed approach, where all the nodes in the mobile network share the same responsibility and act as CHs. The time required to identify the CHs depends on the diameter of the underlying network graph. This method keeps a predefined threshold value for no. of mobile nodes in a cluster. The CH election procedure is invoked only on-demand thus reduces routing control overhead.
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Selection of Weighting Factors in Weighted Clustering Algorithm in MANET

Selection of Weighting Factors in Weighted Clustering Algorithm in MANET

Referencing [6] the weighting factors actually used to normalize the factors such as spreading degree, distance with the neighbors, mobility of the nodes and power consumption by the individual nodes. The combination of individual factors can be appropriately chosen by tuning the weighting factors. The individual weights for individual nodes are given at an interval of 0.05 which varies from 0 to 1 for individual nodes, this is because the weighting factors summation is always equal to 1. The weights incorporated by these nodes should be initially determined rather than substitution according to the requirement of scenario. It has a drawback of not knowing the weights of all the nodes before starting clustering process and draining clusterheads rapidly. Different weights can be tuned at mobility with measurement of throughput and end to end delay and the effect of weighting factors is analyzed. Finally substitute the best suitable weights for selection of clusterhead.
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Privacy-preserving distributed clustering

Privacy-preserving distributed clustering

In [9], Erkin et al. proposed a method based on encryp- tion and secure multiparty computation techniques for clustering users in a centralized system. In that work, Erkin et al. kept the preference vector of each user in the system hidden from all other users and the ser- vice provider and reveal the centroid locations to the service provider for achieving better performance in terms of run-time and bandwidth. The proposed method requires the participation of all users, and the aver- age communication and computation cost is high due to homomorphic encryption. In [10], Beye et al. pro- posed an improved version of K-means clustering by proposing a three-party setting. In that work, users’ pri- vate data are stored by one party and the decryption key by the other. A third party helps with the com- putations. Due to this three-party setting, Beye et al. proposed a highly efficient algorithm based on garbled circuits [20] that does not require oblivious transfer pro- tocols [6]. While the overall system is highly efficient, the authors rely on trusting three separate parties that may not collude.
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AMF-IDBSCAN: Incremental Density Based Clustering Algorithm Using Adaptive Median Filtering Technique

AMF-IDBSCAN: Incremental Density Based Clustering Algorithm Using Adaptive Median Filtering Technique

Density-based spatial clustering of applications with noise (DBSCAN) is a fundamental algorithm for density-based clustering. It can discover clusters of arbitrary shapes and sizes from a large amount of data, which contains noise and outliers. However, it fails to treat large datasets, outperform when new objects are inserted into the existing database, remove noise points or outliers totally and handle the local density variation that exists within the cluster. So, a good clustering method should allow a significant density modification within the cluster and should learn dynamics and large databases. In this paper, an enhancement of the DBSCAN algorithm is proposed based on incremental clustering called AMF-IDBSCAN which builds incrementally the clusters of different shapes and sizes in large datasets and eliminates the presence of noise and outliers. The proposed AMF-IDBSCAN algorithm uses a canopy clustering algorithm for pre-clustering the data sets to decrease the volume of data, applies an incremental DBSCAN for clustering the data points and Adaptive Median Filtering (AMF) technique for post-clustering to reduce the number of outliers by replacing noises by chosen medians. Experiments with AMF-IDBSCAN are performed on the University of California Irvine (UCI) repository UCI data sets. The results show that our algorithm performs better than DBSCAN, IDBSCAN, and DMDBSCAN. Povzetek: V članku je predstavljen nov algoritem AMF-IDBSCAN, izboljšana različica DBSCAN, ki uporablja grozdenje krošenj za zmanjšanje obsega podatkov in tehnike AMF za odpravo hrupa.
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Application of Data Mining in predicting a Course for a Student Based on Previous Records, Financial Status and Personality Traits

Application of Data Mining in predicting a Course for a Student Based on Previous Records, Financial Status and Personality Traits

The number of exams that a student appears and the numbers of courses that are available for him to choose from are increasing day by day and there is clearly a need to organize this data and transform it to information and knowledge. [1] Data mining is a process of extracting hidden or unknown information from data warehouses, databases and data repositories that could be useful. Educational data mining is the branch of data mining that deals with methods for making discoveries from the data that can be obtained from the students’ educational profile, background, behavior, interests, activities in which they participate, etc. Data mining can be applied into educational databases in order to identify potential qualities or specific talents in a student that were previously unknown based on his academic records or extra and co-curricular activities. We can determine courses for the student in which he could excel and give recommendations for the student using data mining techniques. In this paper, we use a widely known tool called GMDH Shell DS and apply K-means clustering algorithm in order to group the students into various categories based on their current academic trends and other records from the past.
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Vol 13, No 1 (2014)

Vol 13, No 1 (2014)

The main function of Cognitive Radio Technology is to enable the Spectrum Utilization and detect the unused spectrum and sharing it without harmful interference to licensed users. Energy Consumption is a primary concern in the Wireless device Networks. The cognitive radio main function is to provide the channel to the user to enable the spectrum resources. The proposed solution is distributed Efficient Multi-Hop Clustering routing protocol which can consider not only for static mobile nodes but also in the Mobile Environment and used to reduce the packet loss during the cluster communication. The main function is to select the cluster head according to the energy level, Connectivity and Stability and transfer the information from the source to the destination. The nodes in the clusters should be advertised the cluster head to other nodes. It improves the Connectivity between the Cluster head and provides the active communication. The DEMC protocol function is to change according to the topology networks and the information stored in the radio networks. It mainly increase the chance of generating the communication link that leads to finds more reliable communication path for Data Transmission.
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E-BEENISH: Enhanced Balanced Energy Efficient Network Integrated Super Heterogeneous Protocol For WSNAbhijit Singh, Shashi B. Rana

E-BEENISH: Enhanced Balanced Energy Efficient Network Integrated Super Heterogeneous Protocol For WSNAbhijit Singh, Shashi B. Rana

energy. The authors supposed that all the nodes of the WSN contain different amount of energy, which is a source of heterogeneity. DEEC is LEACH based algorithm thus it expands the life time of network by rotating the role of CH among all nodes. At the start of processing nodes should have kept the prior knowledge of total energy and lifetime of the network. Reference energy is also known as the average energy of the network. Thus, DEEC does not require any global knowledge of energy at every election round. The routing protocol in [6] is very similar to DEEC. The difference between both lies in the expressions that define the probability for normal and advanced nodes to become a CH. A phase comes during network evolution where the advanced nodes have similar residual energies as the normal nodes. During this phase, DEEC continues to penalize the advanced nodes, which is not an optimal method because by this, the advanced nodes die much faster than the normal nodes. To avoid this unbalance, DDEEC introduces a threshold residual energy. When the energy levels of advanced and normal nodes fall below this threshold residual energy, then same probability is used by all nodes to become a CH, thereby making the CH selection process more efficient. Enhanced Distributed Energy Efficient Clustering or EDEEC [7] uses the concept of three level heterogeneous networks. It consists of three types of nodes-normal, advanced and super nodes-based on initial energies. EDEEC incorporates different probability values for normal, advanced and super nodes. EDDEEC in [8] uses the same concept as of DDEEC but in three types of heterogeneous nodes. The research work is being done in the direction of utilizing four types of heterogeneous sensor nodes in the BEENISH protocol [9]. The selection of cluster head is on the basis of residual energy level of the nodes with respect to the average energy of network as similar to DEEC. However, DEEC is based on two types of nodes; normal and advance nodes. BEENISH uses the concept of four types of nodes; normal, advance, super and ultra-super nodes.
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Clustering EDP (Error Detection Program) Errors from Cloud Data Centres using Data Mining

Clustering EDP (Error Detection Program) Errors from Cloud Data Centres using Data Mining

They described that Hadoop is open source software which can store and manage large files in cloud environment. K-means clustering algorithm is an algorithm used to calculate distance between the centroid of the cluster and the data points. Hashing is algorithm in which we are storing and retrieving data with hash keys. The hashing algorithm is called as hash function which is used to portray the original data and later to fetch the data stored at the specific key. [9] After execution they concluded, by adding hashing them able to access files faster and with the help of encryption, the data stored in the HDFS is safe and secure. Degloved algorithm will not create load on the overall system and smooth retrieving is enabled to the user through which he can get the desired and applicable output. [9]
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