scalable clustering algorithm in data mining

The mountain function is calculated for the vertex point defined as given inwhere is the distance from the data point to the grid nodes and is the positive constant.

It incorporates outlier elimination using sparseness estimation and similarity based possibility C-means (SPCM) algorithm.

The performance results are evaluated using clustering accuracy and immunity to outliers. The ant colony cluster algorithm uses the positive feedback characteristics. A set of relevant features shows the accuracy of the cluster, which has potential applications in e-commerce [8], a computer vision task [9]. While the proposed framework allows a large class of algorithms to be used for clustering the sampled set, we focus on some popular parametric algorithms for ease of exposition. The authors declare no conflicts of interest. sigkdd ramakrishnan raghu Read the winning articles.

Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. To find the cluster centers the mountain function is defined as given inwhere is taken as the maximal value of mountain function and and are positive constants. The main idea behind the development of PCM SPCM based application is to integrate with mountain cluster. In addition, several data mining applications demand that the clusters obtained be balanced, i.e., of approximately the same size or importance.

Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence, Department of Computer Applications, Gnanamani College of Technology, AK Samuthiram, Pachal, Namakkal District, Tamil Nadu 637 018, India, Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu 638 052, India, R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, Automatic subspace clustering of high dimensional data,, C. C. Aggarwal, J. L. Wolf, P. S. Yu, C. Procopiuc, and J. S. Park, Fast algorithms for projected clustering, in, M. Ester, H. P. Kriegel, and X. Xu, A database interface for clustering in large spatial databases, in, T. Zhang, R. Ramakrishnan, and M. Livny, BIRCH: an efficient data clustering method for very large databases, in, K. Y. Yip, D. W. Cheung, and M. K. Ng, A review on projected clustering algorithms,, S. Guha, R. Rastogi, and K. Shim, CURE: an efficient clustering algorithm for large databases, in, H. Liu and L. Yu, Toward integrating feature selection algorithms for classification and clustering,, M. Bouguessa and S. Wang, Mining projected clusters in high-dimensional spaces,, M. L. Yiu and N. Mamoulis, Iterative projected clustering by subspace mining,, K. Y. L. Yip, D. W. Cheung, and M. K. Ng, HARP: a practical projected clustering algorithm,, E. Ng, A. Fu, and R. Wong, Projective clustering by histograms,, C. M. Procopiuc, M. Jones, P. K. Agarwal, and T. M. Murali, A Monte Carlo algorithm for fast projective clustering, in, H. Wang, W. Wang, J. Yang, and P. S. Yu, Clustering by pattern similarity in large data sets, in, V. S. Tseng and C.-P. Kao, A novel similarity-based fuzzy clustering algorithm by integrating PCM and mountain method,, J. Venkatesh, K. Sridharan, and S. B. Manooj Kumaar, Location based services prediction in mobile mining: determining precious information,, R. T. Ng and J. Han, Efficient and effective clustering methods for spatial data mining, in, R. R. Yager and D. P. Filev, Approximate clustering via the mountain method,, M. Dorigo and K. Socha, An introduction to ant colony optimization, in, K. Y. Yip, D. W. Cheung, M. K. Ng, and K. H. Cheung, Identifying projected clusters from gene expression profiles,. Few of them are CLIQUE, DOC, Fast DOC, PROCLUS, ORCLUS, and HARP [10]. American Century Investments, Kansas City, MO.

However, datasets with mixed types of attributes are common in real life data mining problems.

In addition to providing balancing guarantees, the clustering performance obtained using the proposed framework is comparable to and often better than the corresponding unconstrained solution. T1 - Scalable clustering algorithms with balancing constraints. The proposed technique achieves higher accuracy for both datasets when compared with existing PCKA technique. In this paper, we propose a distance measure that enables clustering data with both continuous and categorical attributes. x[r6c?>M{Z;33v}UEIW2jM5 HJvDwH@_~ ]w(w^Rp.+&+!5cW+u~_;e*mnS\+?.]i[1^xm1U)fnE#+)r\K?n\2|_NrF8wqi5)*S7a#Js;5Ok~Yr?i:b5*au?+tS;.M)b)Q}d*ZsOt\eOox~f9Z{QU-Kn|eYJ"9L{/XV=|uzy~ P80vY08 n1c+H?CYU[Vq$FLj,W)sC72nkCnsA(10w<6^q@V&A% T#H>0b*ozM"$`A0M\=^3T!5OO'xZ?j\87xzjck @ a\^k T1pL_Y}axpz z_@:B|N)Fm&+I^1d?1C6zx;0&KsL1]" s\^,1s/F`AH?X24 qK2^RuX1RHkakf

We then present algorithms to populate and refine the clusters. This solves the problem of other fuzzy clustering methods which deals with similarity based clustering applications in the sets.

The algorithm is implemented in the commercial data mining tool Clementine 6.0 which supports the PMML standard of data mining model deployment.

Copyright 2008 Elsevier B.V., All rights reserved. The proposed methodology is focused to cluster high-dimensional data in projected space. First, we show that a simple uniform sampling from the original data is sufficient to get a representative subset with high probability. Dorigo and Socha [19] who proposed ant colony system solve combinatorial optimization problems.

[15]. Using the cluster structure, a region having high density of points is chosen compared to its surrounding regions, which represent the 1D projection of clusters.

For this a new class of projected clustering arises in this technique.

Apart from the ability of handling mixed type of attributes, our algorithm differs from BIRCH in that we add a procedure that enables the algorithm to automatically determine the appropriate number of clusters and a new strategy of assigning cluster membership to noisy data.

N2 - Clustering methods for data-mining problems must be extremely scalable.

First, we show that a simple uniform sampling from the original data is sufficient to get a representative subset with high probability. Prediction in mobile mining for location based services to determine precious information is studied by Venkatesh et al.

While the proposed framework allows a large class of algorithms to be used for clustering the sampled set, we focus on some popular parametric algorithms for ease of exposition. The advantage of HARP is that it determines automatically the dimensions for each cluster without the use of input parameters, whose values are difficult to define.

Moreover these methods often show good noise-handling capabilities, where clusters are defined as regions of typical densities separated by low or no density regions. These techniques increase the speed of clustering algorithms and hence performance is improved [6]. Copyright 2015 Thenmozhi Srinivasan and Balasubramanie Palanisamy. An efficient high-dimensional dataset clustering is proposed in this work with its optimized results. Research output: Contribution to journal Article peer-review.

In this paper, we propose a general framework for scalable, balanced clustering. Scalable clustering algorithms with balancing constraints. Scalable Varied Density Clustering Algorithm for Large Datasets, A. Fahim, A. Salem, F. Torkey, M. Ramadan and G. Saake, "Scalable Varied Density Clustering Algorithm for Large Datasets,". i !@%_!2c3E(/ps3ICMS3 First, we show that a simple uniform sampling from the original data is sufficient to get a representative subset with high probability. Given a data set with n records in a d-dimensional space, the cost of applying a clustering algorithm to partition the data set into k clusters is a function of n, k, and d. In the situation that n is large but k is small and the situation that both n and k are large, scalable clustering algorithms are needed. Copyright 2022 ACM, Inc. A robust and scalable clustering algorithm for mixed type attributes in large database environment, Washington ,

This involves a distance metric, in which the data points in each partition are similar to points in different partitions. journal = "Data Mining and Knowledge Discovery", Scalable clustering algorithms with balancing constraints, https://doi.org/10.1007/s10618-006-0040-z. Herein a new scalable clustering technique which addresses all these issues is proposed. SPCM has merit that it can automatically produce results clustering without requiring users to determine the number of clusters.

We then present algorithms to populate and refine the clusters.

Data mining deals with extracting useful information from datasets [1]. In attribute relevance analysis, cluster structures are displayed by identifying dense regions and their location in each dimension.

note = "Funding Information: The research was supported in part by NSF grants IIS 0325116, IIS 0307792, and an IBM PhD fellowship.

This distance measure is derived from a probabilistic model that the distance between two clusters is equivalent to the decrease in log-likelihood function as a result of merging. @article{e92a7b51a5414d8e8063d685d3b9af3e. N1 - Funding Information: By detecting dense regions in each dimension the discrimination between dimensions that are relevant and irrelevant can be detected, and sparseness degree is then computed to detect densely populated regions in each attribute.

For projected clustering, a cluster must contain relevant dimensions of the data in which the projection of each point of the cluster is close to a sufficient number of other projected points.

There is no dependency for the membership degree on the same data objects in other clusters. By identifying signatures for a large number of data points [13], projected clusters are uncovered. Ant colony cluster [20] is motivated by the accumulation of ant bodies and ant larvae classification.

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Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Experimental results on several datasets, including high-dimensional (>20,000) ones, are provided to demonstrate the efficacy of the proposed framework.".

In addition to providing balancing guarantees, the clustering performance obtained using the proposed framework is comparable to and often better than the corresponding unconstrained solution.

Then data points are initialized as. Clustering is a technique that is required for various applications in pattern analysis, decision making, group and machine document retrieval, learning environment, pattern classification, and image segmentation. The data clustering process is broken down into three steps: sampling of a small representative subset of the points, clustering of the sampled data, and populating the initial clusters with the remaining data followed by refinements. We use cookies to ensure that we give you the best experience on our website. There are various approaches proposed for projected clustering in the past. xt`tIST @xpof+ 7ar$7-0cHs#Mk5\0Dnd0)$j f )'PO#*em%*.2B*G*i|l+@+hN&R?mL.VAzgx/0b/ 5/OrX1b s}_sL$//.

Experimental results on several datasets, including high-dimensional (>20,000) ones, are provided to demonstrate the efficacy of the proposed framework. Experimental results on several datasets, including high-dimensional (>20,000) ones, are provided to demonstrate the efficacy of the proposed framework. The cluster will see a considerable number of aspects related to the above relationship in which points are close to each other in a large number. The data clustering process is broken down into three steps: sampling of a small representative subset of the points, clustering of the sampled data, and populating the initial clusters with the remaining data followed by refinements. Dive into the research topics of 'Scalable clustering algorithms with balancing constraints'. HARP works with the assumption that if the data points are similar in high-dimensional space, they also show the same similarity in lower-dimensional space.

This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM), with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. Copyright: A large number of data mining techniques to cluster the data are available. In data mining, the purpose of data clustering is to identify useful patterns in the underlying dataset. A projected hierarchical clustering algorithm called hierarchical approach with automatic relevant dimension selection (HARP) is proposed in [10]. We then present algorithms to populate and refine the clusters.

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Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets.

The cluster centers are selected by the nodes having the maximum number of mountain functions.

Experimental results on several datasets, including high-dimensional (>20,000) ones, are provided to demonstrate the efficacy of the proposed framework.

Step 5.

Zhang et al [8] proposed a clustering method named BIRCH that is especially suitable for very large datasets. The authors declare that there is no conflict of interests regarding the publication of this paper. In data mining, clustering is a process which recognizes similar description (homogenized) groups of data on the basis of their size (profile). Fuzzy clustering method (FCM) [14] is based on partition. EPCH is a type of compression based clustering algorithm; it is faster and can handle irregular clusters, but in full-dimensional space it cannot compute distance between data points.

The similarity and minimum number of similar dimensions can be controlled dynamically, without the help of parameters given by user. This reduces the burden of clustering algorithm. The complexity of the overall method is O(kN log N) for obtaining k balanced clusters from N data points, which compares favorably with other existing techniques for balanced clustering.

Clustering the dataset includes measures distance or similarity measure to partition the dataset, where data inside the clusters is similar to data outside the cluster. <> For data with mixed type of attributes, our experimental results confirm that the algorithm not only generates better quality clusters than the traditional k-means algorithms, but also exhibits good scalability properties and is able to identify the underlying number of clusters in the data correctly. The results given in the above section show its efficient clustering accuracy and outlier detection. The data clustering process is broken down into three steps: sampling of a small representative subset of the points, clustering of the sampled data, and populating the initial clusters with the remaining data followed by refinements.

The data clustering process is broken down into three steps: sampling of a small representative subset of the points, clustering of the sampled data, and populating the initial clusters with the remaining data followed by refinements.

In this paper, we propose a general framework for scalable, balanced clustering. In this chapter, we will review and present some algorithms these situations. The algorithm for populating the clusters is based on a generalization of the stable marriage problem, whereas the refinement algorithm is a constrained iterative relocation scheme. The algorithm for populating the clusters is based on a generalization of the stable marriage problem, whereas the refinement algorithm is a constrained iterative relocation scheme. abstract = "Clustering methods for data-mining problems must be extremely scalable. The research was supported in part by NSF grants IIS 0325116, IIS 0307792, and an IBM PhD fellowship. It is robust and helps to produce scalable clustering. In addition to providing balancing guarantees, the clustering performance obtained using the proposed framework is comparable to and often better than the corresponding unconstrained solution.

The results were analyzed through performance evaluation based on real datasets which are synthetic.

When the data is a set of samples drawn from stationary processes, a framework for defining consistency of clustering algorithms is proposed in [17].

The experiment is evaluated using the synthesis dataset implemented in MATLAB R2012 platform. In those cases, reducing the dimensions using conventional feature selection leads to significant loss of data. The ants observing radius is the main factor that influences this process, and the cluster is more efficient when radius is small [4].

Mountain method discretizes the feature space which forms an -dimensional grid in hypercube with nodes , where chooses values from the set . The complexity of the overall method is O(kN log N) for obtaining k balanced clusters from N data points, which compares favorably with other existing techniques for balanced clustering. This work is done based on the block diagram shown in Figure 1. The algorithm for populating the clusters is based on a generalization of the stable marriage problem, whereas the refinement algorithm is a constrained iterative relocation scheme.

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DC , The complexity of the overall method is O(kN log N) for obtaining k balanced clusters from N data points, which compares favorably with other existing techniques for balanced clustering. The ACM Digital Library is published by the Association for Computing Machinery.

Spatial data [16] defines a location, shape, size, and orientation and it includes spatial relationships whereas nonspatial data is information which is independent of all geometric considerations.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Table 1 shows the clustering accuracy of the proposed and existing technique for the WDBC and MF dataset.

title = "Scalable clustering algorithms with balancing constraints". No related content is available yet for this article. Thus the ant conveying process is a simple, flexible, easy, and absolute individual behavior where objects are distributed and divided into several clusters during long time, subsequently as a concurrent process. / Banerjee, Arindam; Ghosh, Joydeep. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets. An absolute degree of data object and membership function in a cluster is assumed. And the optimized clustering result is obtained by using ant colony optimized technique with swarm intelligence.

EDBSCAN, Data Clustering, Varied Density Clustering, Cluster Analysis. When the observing radius is large, the algorithms convergence speed in return is expedited.

This work is composed of SPCM technique which finds the clusters automatically without users input of number of clusters.

Clustering nonspatial data using similarity based PCM (SPCM) is proposed in [14]. After identification of clusters, by selecting appropriate dimensions the result is refined.

The algorithm for populating the clusters is based on a generalization of the stable marriage problem, whereas the refinement algorithm is a constrained iterative relocation scheme.

Thenmozhi Srinivasan, Balasubramanie Palanisamy, "Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence", The Scientific World Journal, vol. Among all these proposed methods, density clustering methods are the most important due to their high ability to detect arbitrary shaped clusters.

In this paper, we propose a general framework for scalable, balanced clustering. By continuing you agree to the use of cookies. %%_G@%| DBsw)Q@lA)t2,`+l@QQ Oc;2ch(]rjN"AMW6 A projected clustering is also called subspace clustering [7] which has high-dimensional datasets, a unique group of data points that are correlated with different sets of dimensions, where the focus is to determine a set of attributes for each cluster. The main advantage of traditional ant cluster algorithm is the adjustment of observing radius and the ants memory function, and it also benefits in magnitude ameliorating aspect. Clustering is a widely used technique in data mining applications to discover patterns in the underlying data. The dimensions identified represent potential candidates for the clusters. Calculation of this measure is memory efficient as it depends only on the merging cluster pair and not on all the other clusters. This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License. In addition, several data mining applications demand that the clusters obtained be balanced, i.e., of approximately the same size or importance.

It can be expressed aswhere is assigned to a value of one.

In addition, several data mining applications demand that the clusters obtained be balanced, i.e., of approximately the same size or importance. We then present algorithms to populate and refine the clusters. Most traditional clustering algorithms are limited to handling datasets that contain either continuous or categorical attributes. Let the dataset be taken as DS, having -dimensional points, where attributes are denoted as . Depending on this criterion, if clusters are similar in various numbers of dimensions, they are allowed to merge. In general spatial data are multidimensional and autocorrelated and nonspatial data are one-dimensional and independent. %PDF-1.2 Copyright 2006-2022 Scientific Research Publishing Inc. All Rights Reserved.

The scope of further research is to deal with datasets that have a large number of dimensions. The advantage of PCM is the membership function and the number of clusters is independent, and in a noisy environment with outliers it is highly vigorous [4].

Together they form a unique fingerprint. Let denote the th coordinate of the th point, where and .

Where the selection of feature techniques reduces dimensions by removing irrelevant features they may have to eliminate many of the features associated with the presence of sporadic failure.

2015, Article ID 107650, 5 pages, 2015. https://doi.org/10.1155/2015/107650, 1Department of Computer Applications, Gnanamani College of Technology, AK Samuthiram, Pachal, Namakkal District, Tamil Nadu 637 018, India, 2Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, Erode, Tamil Nadu 638 052, India.