We demonstrate our visualization techniques on two real datasets. The first dataset is a zoo dataset (D.J. Newman and Merz, 1998). The Zoo Database contains 101 instances and 18 attributes (animal name, 15 boolean attributes, 2 numerics). The attributes are hair, feathers, eggs, milk, airborne, aquatic, predator, toothed, backbone, breathes, venomous, fins, tail, domestic and catsize. And the numeric attributes are legs and type, where the type attribute appears to be the class attribute. All the instances are classified into 7 classes. We consider this as a binary dataset and applied itemset mining on it. Though small, the set of subspace clusters may generate over 600 subspace clusters. And it is hard to find useful classification information from the 600 clusters. In addition, those clusters exhibits other characteristics of subspace clusters, such as overlap and incompleteness(No perfect classification of any cluster). The second dataset is a yeast gene expression dataset. It contains 2884 genes and 17 conditions. These genes were selected according to Spellman et.al.(Spellman et al., 1998) The range of the gene expression value are between 0 and 600. One of the objectivity of two datasets of this **data** is to find the co-regulated genes under a subset rather than the whole sets of conditions. We apply the δ−pCluster to the dataset. By varying the p-score threshold and minimum number of genes within a cluster, the maximum number of clusters easily exceeds 5000.

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DBSCAN (Density-Based spatial cluster of Applications with Noise) may be a density primarily based cluster rule which can generate any variety of clusters, and additionally for the distribution of spatial information [1].To convert great amount of **data** into separate clusters in order to better and faster access is the main purpose of cluster rule. The rule grows regions with sufficiently **high** density into clusters and discovers clusters of arbitrary form in spatial databases with noise. It defines a cluster as a highest set of density- connected points. Set of density-connected objects that's highest with respect to density-reach ability may be a density-based cluster. As every object not contained in any cluster for considering the noise. DBSCAN method is sensitive to its parameter e and Min Pts, and leaves the user with the responsibility of selecting parameter values that will cause the discovery of acceptable clusters. The machine complexness of DBSCAN is O(nlogn) if a spatial index is employed, wherever n is the range of info objects. Otherwise, it's O(n2).

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In this paper work shown that using fuzzy based kernel mapping to approximate local **data** centers is not only a feasible option, but also frequently leads to improvement over the centroid-based approach. the proposed the fuzzy based k-means and kernel mappings with consensus neighbouring **clustering** in **high** **dimensional** **data** algorithm for the consensus **clustering** algorithm is in core variations of fuzzy based consensus neighbouring **clustering** algorithm using different weight measures applied to the vector of base-level clustering’s baseline on both synthetic and real-world **data**, as well as in the presence of **high** levels of artificially introduced noise. The kernel map with consensus neighbour **clustering** can easily be extended to incorporate additional pair-wise constrains such as requiring points with the same label to come into view in the same cluster with just an extra layer of function hubs. A further challenge is to identify scenarios where the use of soft ensembles provides significantly improved performance over hard ensembles, and if needed devise specialized algorithms to deal with various domains such as medical domains.

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Skyline queries are used in a variety of fields to make optimal decisions. However, as the volume of **data** and the dimension of the **data** increase, the number of skyline points increases with the amount of time it takes to discover them. Mainly, because the number of skylines is essential in many real-life applications, various studies have been proposed. However, previous researches have used the k-parameter methods such as top-k and k-means to discover representative skyline points (RSPs) from entire skyline point set, resulting in **high** query response time and reduced representativeness due to k dependency. To solve this problem, we propose a **new** Connected Component **Clustering** based Representative Skyline Query (3CRS) that can discover RSP quickly even in **high**-**dimensional** **data** through connected component **clustering**. 3CRS performs fast discovery and **clustering** of skylines through hash indexes and connected components and selects RSPs from each cluster. This paper proves the superiority of the proposed method by comparing it with representative skyline queries using k-means and DBSCAN with the real-world dataset.

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Rapid growth of **high** **dimensional** datasets in recent years has created an emergent need to extract the knowledge underlying them. **Clustering** is the process of automatically finding groups of similar **data** points in the space of the dimensions or attributes of a dataset. Finding clusters in the **high** **dimensional** datasets is an important and challenging **data** mining problem. **Data** group together differently under different subsets of dimensions, called subspaces. Quite often a dataset can be better understood by **clustering** it in its subspaces, a process called subspace **clustering**. But the exponential growth in the number of these subspaces with the dimensionality of **data** makes the whole process of subspace **clustering** computationally very expensive. There is a growing demand for efficient and scalable subspace **clustering** solutions in many Big **data** application domains like biology, computer vision, astronomy and social networking. Apriori based hierarchical **clustering** is a promising approach to find all possible higher **dimensional** subspace clusters from the lower **dimensional** clusters using a bottom-up process. However, the performance of the existing algorithms based on this approach deteriorates drastically with the increase in the number of dimensions. Most of these algorithms require multiple database scans and generate a large number of redundant subspace clusters, either implicitly or explicitly, during the **clustering** process. In this paper, we present SUBSCALE, a novel **clustering** algorithm to find non-trivial subspace clusters with minimal cost and it requires only k database scans for a k-**dimensional** **data** set. Our algorithm scales very well with the dimensionality of the dataset and is highly parallelizable. We present the details of the SUBSCALE algorithm and its evaluation in this paper.

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This paper implements a **high**-**dimensional** projected stream **clustering** method by means of continuous refinement of the set of projected dimensions and **data** points all through the progression of the stream this is called as HPStream, since it describes the **High**- **dimensional** Projected Stream **clustering** method. The updating of the set of dimensions related to each cluster is carried out in such a way that the points and dimensions related to each cluster can efficaciously evolve through the time. In order to obtain this goal, using the condensed representation of the statistics of the points in the clusters. These condensed representations are selected in the sort of manner that they can be update effectively in a fast **data** stream. At the same time, a sufficient amount of information is stored in order that essential measures about the cluster in a given projection can be quickly computed. The fading cluster structure is also capable of performing the updates in this such a way that previous **data** is temporally discounted. This guarantees that during an evolving **data** stream, the beyond history is progressively discounted from the computation. HPStream introduces the technic of projected **clustering** to **data** streams and fading cluster structure.

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ABSTRACT- Citrus industry contributes a major part in nation’s growth, but there has been a decrease in production of good quality citrus fruits, due to improper cultivation, lack of maintenance, very **high** post harvest losses in handling and processing, manual inspection, lack of knowledge of preservation and quick quality evaluation techniques. Unrelated features, along with repetitive features, severely affect the accuracy of the learning machines. So, feature subset collection should be able to identify and remove as much of the irrelevant and redundant information as possible. A feature selection algorithm may be evaluated from both the efficiency and effectiveness. The efficiency relates to the time spend to find only relevant features from collection, the effectiveness concerns to the quality of the required features. Based on these conditions, an improved **clustering**-based feature selection algorithm is experimented. The improved/efficient **clustering** methods are implemented in two stages. In the first stage, features are divided into clusters by using graph-theoretic **clustering** algorithms. In the second stage, the important feature that is strongly related to target classes is selected from each cluster to form a subset of features. The efficiency of the effective **clustering** algorithm are evaluated through an empirical study. The specific objectives implemented to accomplish is: collect images from citrus leaves of three common citrus diseases, and normal leaves; determine color co-occurrence method texture features for each image in the dataset; Apply effective **Clustering** and Classification to retrieve feature **data** models. In this paper, determine the effective **clustering**/ classification accuracies using a performance measure for feature extraction in citrus fruits and leaves.

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• The sync coordinator collects these messages and maintain a global view of the clusters. Meanwhile it also counts the total number of protomemes processed. When the batch size is reached, it broadcast SYNCINIT to all **clustering** bolts to tell them temporarily stop protomeme processing and do synchronization.

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Abstract—We introduce Cloud DIKW as an analysis environment supporting scientific discovery through integrated parallel batch and streaming processing, and apply it to one representative domain application: social media **data** stream **clustering**. In this context, recent work demonstrated that **high**- quality clusters can be generated by representing the **data** points using **high**-**dimensional** vectors that reflect textual content and social network information. However, due to the **high** cost of similarity computation, sequential implementations of even single-pass algorithms cannot keep up with the speed of real- world streams. This paper presents our efforts in meeting the constraints of real-time social media stream **clustering** through parallelization in Cloud DIKW. Specifically, we focus on two system-level issues. Firstly, most stream processing engines such as Apache Storm organize distributed workers in the form of a directed acyclic graph (DAG), which makes it difficult to dynamically synchronize the state of parallel **clustering** workers. We tackle this challenge by creating a separate synchronization channel using a pub-sub messaging system (ActiveMQ in our case). Secondly, due to the sparsity of the **high**-**dimensional** vectors, the size of centroids grows quickly as **new** **data** points are assigned to the clusters. As a result, traditional synchronization that directly broadcasts cluster centroids becomes too expensive and limits the scalability of the parallel algorithm. We address this problem by communicating only dynamic changes of the clusters rather than the whole centroid vectors. Our algorithm under Cloud DIKW can process the Twitter 10% **data** stream (“gardenhose”) in real-time with 96-way parallelism. By natural improvements to Cloud DIKW, including advanced collective communication techniques developed in our Harp project, we will be able to process the full Twitter **data** stream in real-time with 1000-way parallelism. Our use of powerful general software subsystems will enable many other applications that need integration of streaming and batch **data** analytics.

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Fast algorithm employs the **clustering**-based method to choose features. General framework as shown in Fig. 1 in which irrelevant features are removed first and then to remove redundant features minimum spanning tree is constructed and then tree partitioning is used to obtain the selected features. Fast Algorithm can eliminate the irrelevant features effectively but it is ineffective at removing redundant features which affect the speed and accuracy of algorithm, thus it should be eliminated as well.

The NBC [7] (Neighbourhood-Based **Clustering**) **clustering** algorithm also belongs to the class of density-based **clustering** algorithms. NBC algorithm can discover arbitrary shape clusters, and it requires fewer input parameters than the existing algorithms. NBC algorithm can cluster **high**- **dimensional** **data** sets efficiently. OPTICS [8] (Ordering Points to Identify the **Clustering** Structure) extends DBSCAN in order to cluster **data** points from a range of parameter settings. OPTICS algorithm differentiates considerable objects from outliers or noise thereby identifying all cluster levels in a **data** set. DENCLUE [9] is a **clustering** algorithm that applies the kernel density estimation to employ a cluster model, and clusters the object found on the set of density distribution function. The algorithm uses the idea of density attracted regions to form clusters. However, the algorithm is not suitable for **data** sets of **high** dimension.

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Hornik et al. (2012) have presented the theory underlying the standard spherical K-means problem and suitable extensions, and introduced the R extension package skmeans which provided a computational environment for spherical K-means **clustering** featuring several solvers: a fixed-point and genetic algorithm, and interfaces to two external solvers (CLUTO and Gmeans). Performance of these solvers is investigated by means of a large scale an experiment. A large scale benchmark experiment analyzing the performance and efficiency of the available solvers have showed that the presented **approaches** scaled well and could be used for realistic **data** sets with an acceptable **clustering** performance. The external solvers Gmeans and CLUTO are both very fast, with CLUTO typically providing better solutions. The genetic algorithm is found excellent solutions but has the longest runtime, whereas the fixed-point algorithm is a very good all-round approach.

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In this paper, proposed method of KMNC method had proven to be more robust than the GHPKM and K-Means++ baseline on both synthetic and real-world **data**, as well as in the presence of **high** levels of artificially introduced noise. The kernel map with neighbor **clustering** can easily be extended to incorporate additional pair- wise constrains such as requiring points with the same label to come into view in the same cluster with just an extra layer of function hubs. The model is flexible enough for information other than explicit constraints such as two points being in different clusters or even higher-order constraints (e.g., two of three points must be in the same cluster).

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In our paper we will make use of so-called polynomial threshold functions (PTF), a powerful tool developed by (Alman, Chan, and Williams 2016). PTFs are distributions of polynomials that can efficiently evaluate certain types of Boolean formulas with some probability. They are mainly used to solve problems in circuit theory, but were also used to develop **new** algorithms for other problems such as ap- proximate all nearest neighbors or approximate minimum spanning tree in Hamming, ` 1 and ` 2 spaces. In the follow-

In a general sense, the **data**-**clustering** task aims to find clusters according to a similarity measure, in a manner that the **data** instances from a related cluster possess **high** similarity, while the **data** instances from different clusters possess low similarity [Aggarwal and Reddy 2013]. To compute this measure it is necessary to have a description of the **data** instances and a distance function. Thus, depending on the **data** domain, the instances can be described by a set of attributes of traditional domains (for example, text or numbers) or by a collection of pre-defined feature descriptors inherent to the **data** (considering the image domain, for example: color, texture and shape, among other features). The choice of a distance function to be employed also depends on the deployed **data** domain, being that among the most commonly used are the distance functions from the Minkowski family [Taniar and Iwan 2011].

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It [6] combined the Newton Raphson method and iterative projection together to learn a Mahalanobis distance for K-means **clustering**. It [4] proposed a more efficient algorithm for learning the distance metric with side information, which utilized Canonical Correlation Analysis (CCA) to approximate LDA. In general, the metric learning used in the distance based method, which is equivalent to learning an adaptive weight for each dimension, is either based on iterative algorithms, such as gradient descent and Newton’s method, or involves some matrix operations. However, the distance based method has **high** computational cost when applied to the **high**-**dimensional** **data**. Indeed, **data** represented in matrix is often singular when the sparsity of the **data** is **high**. This makes some matrix operations, such as inversion, computationally intractable. For hybrid methods, [5] introduced a general probabilistic framework which unifies the constraint-based and distance-based method into the Hidden Markov Random Field (HMRF).

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ensemble approach. Our major contribution is the development of an incremental ensemble member selection process based on a global objective function and a local objective function. To design a good local objective function, we also propose a **new** similarity function to quantify the extent to which two sets of attributes in the subspaces are similar to each other. We conduct experiments on six real-world datasets from the UCI machine learning repository and 12 real world datasets of cancer gene expression profiles, and obtain the following: The incremental ensemble member selection process is a general technique which can be used in different semi-supervised **clustering** ensemble **approaches**. The prior knowledge represented by the pair wise constraints is useful for improving the performance of ISSCE. ISSCE outperforms most conventional semi-supervised **clustering** ensemble **approaches** on many datasets, especially on **high** **dimensional** datasets. In the future, we shall perform theoretical analysis to further study the effectiveness of ISSCE, and consider how to combine the incremental ensemble member selection process with other semi supervised **clustering** ensemble **approaches**. We shall also investigate how to select parameter values depending on the structure/complexity of the datasets.

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methods incorporate feature selection as a part of the training process and are usually specific to given learning algorithms, and therefore may be more efficient than the other three categories. Traditional machine learning algorithms like decision trees or artificial neural networks are examples of embedded **approaches**. The wrapper methods use the predictive accuracy of a predetermined learning algorithm to determine the goodness of the selected subsets, the accuracy of the learning algorithms is usually **high**. However, the generality of the selected features is limited and the computational complexity is large. The filter methods are independent of learning algorithms, with good generality. Their computational complexity is low, but the accuracy of the learning algorithms is not guaranteed. The hybrid methods are a combination of filter and wrapper methods by using a filter method to reduce search space that will be considered by the subsequent wrapper. They mainly focus on combining filter and wrapper methods to achieve the best possible performance with a particular learning algorithm with similar time complexity of the filter methods. The wrapper methods are computationally expensive and tend to over fit on small training sets. The filter methods, in addition to their generality, are usually a good choice when the number of features is very large. Thus, we will focus on the filter method in this paper. With respect to the filter feature selection methods, the application of cluster analysis has been demonstrated to be more effective than

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Euclidean separation: In an exceptionally inadequate and **high**-**dimensional** space like content records, circular k-means, which utilizes cosine closeness (CS) rather than Euclidean separation as the measure, is considered to be more suitable .In, Banerjee et al. demonstrated that Euclidean separation was without a doubt one specific manifestation of a class of separation measures called Bregman divergences. They proposed Bregman hard bunching calculation, in which any sort of the Bregman divergences could be connected. Kullback-Leibler dissimilarity was an extraordinary instance of Bregman divergences that was said to give great grouping comes about on archive information sets. Kullback-Leibler uniqueness is an exceptional illustration of nonsymmetrical measure. Likewise on the point of catching disparity in information, Pakalska et al. discovered that the discriminative force of some separation measures could expand when their non- Euclidean and nonmetric traits were expanded. They inferred that non-Euclidean and nonmetric measures could be

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DENCLUE [16] is a generalization of DBSCAN and K-means. It works in two stages as pre-processing stage and **clustering** stage. In pre-processing step, it creates a grid for the **data** by dividing the minimal bounding hyper-rectangle into d- **dimensional** hyper-rectangles with edge length 2σ. In the **clustering** stage, DENCLUE associates an “influence function” with each **data** point and the overall density of the dataset is modelled as the sum of influence functions associated with each point. The resulting general density function will have local peaks, i.e., local density maxima, and these local peaks can be used to define clusters. If two local peaks can be connected to each other through a set of **data** points, and the density of these connecting points is also greater than a minimum density threshold ξ, then the clusters associated with these peaks are merged forming the clusters of arbitrary shape and size. The performance of DENCLUE is appealing in low **dimensional** space, however, it does not work well as the dimensionality increase or if noise is present.

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