Partitional Clustering Techniques Research Articles

Intelligent mapping of geochemical anomalies: Adaptation of DBSCAN and mean-shift clustering approaches

Cluster analysis can be used to organize samples and generate ideas regarding the multivariate geochemistry of given dataset. Traditional clustering techniques have the drawbacks of high computational complexity and poor adaptability to big data. Hence, there has recently been much focus on creating better clustering algorithms. Although many clustering algorithms have been applied, and some produce notable clustering results, the performance efficiency of algorithms is often highly dependent on the values the user chooses for the parameters. Currently, density-based spatial clustering of applications with noise clustering (DBSCAN) is widely utilized in image processing, bioinformatics, and social network analysis owing to its ability to detect clusters of various shapes. Even though partitional clustering techniques may be effective when the number of clusters K is known in advance, they cannot implement non-convex clustering and rapidly return to a local optimum. This study proposes the concept of DBSCAN clustering for stream sediment geochemical data. In this respect, the geochemical data collected from Varcheh district, SW Iran, were processed using the clr transformation before applying DBSCAN. Then, PCA was used to minimize the dimension of variables and specify the mineralization-related elements. In the following, one of the PCs connected with mineralization (PC2) was chosen for further analysis. DBSCAB, Mean-shift and Fuzzy K-means algorithms were used to monitor the multi-element geochemical anomalies linked to MVT PbZn deposits in the study area. According to Davies-Bouldin and Silhouette as two validation metrics, it can be deduced that the three SCB models are advantageous, however, the model generated by DBSCAN is preferable to the model generated by Mean-shift and Fuzzy K-means.

A semi-supervised hierarchical ensemble clustering framework based on a novel similarity metric and stratified feature sampling

Recently, both ensemble clustering and semi-supervised clustering have emerged as important paradigms of traditional clustering. Ensemble clustering seeks to integrate multiple clustering results from different methods or the same methods with different parameters. Semi-supervised clustering involves using a small amount of class membership information in some samples for the learning process. Meanwhile, Semi-Supervised Ensemble Clustering (SSEC) has attracted increasing attention due to its high performance. However, most SSEC algorithms are configured based on partitional clustering techniques, and there are few attempts on hierarchical clustering techniques. Even in existing hierarchy-based SSEC algorithms, prior knowledge is not sufficiently used and is often applied to create primary partitions. To address these problems, we propose a Semi-supervised Hierarchical Ensemble Clustering framework based on a novel Similarity metric and stratified feature Sampling, which we call SHECSS. SHECSS uses the information of all primary partitions according to their strength to calculate the similarity between samples. Also, SHECSS is equipped with a stratified feature sampling mechanism that can improve the diversity of primary partitions and deal with high-dimensional data. Here, the primary partitions are created based on multiple hierarchical clustering techniques, and the target partition is configured by a consensus function based on the clusters clustering policy. Experimental results show the effectiveness and efficiency of SHECSS compared to representative clustering methods.

A fast [formula omitted] time hybrid clustering algorithm using the circumference proximity based merging technique for diversified datasets

Clustering has been widely employed for extracting intrinsic groups because of its low reliance on domain knowledge. Though several clustering techniques have been developed in the literature, the majority of them become inefficient due to their dependence on user-defined parameters or inability to cluster multi-scale datasets. To handle these issues, several hybrid clustering algorithms which combine the advantages of partitional, hierarchical, and graph-based clustering techniques have been developed. Hybrid clustering algorithms first partition the data into smaller clusters and then merge them into genuine clusters. However, most of them incur a quadratic time complexity which limits their application for large datasets. The proposed work presents a two-step fast hybrid clustering algorithm based on partitions and efficient merging to reduce the computational cost by maintaining the clustering quality. In the first step, the dispersion of the data is considered to produce balanced partitions. Then graph circumference proximity-based merging technique is proposed to merge the sub-clusters. The overall computational complexity of the algorithm is O(NlgN) where N is the number of data points, ignoring the dimension. To the best of our knowledge, this is the fastest known graph neighborhood-based hybrid clustering algorithm. Experimental results on various diversified datasets exhibit a significant improvement in the running time as well as cluster quality and robustness against noise.

A survey on the utilization of Superpixel image for clustering based image segmentation.

Superpixel become increasingly popular in image segmentation field as it greatly helps image segmentation techniques to segment the region of interest accurately in noisy environment and also reduces the computation effort to a great extent. However, selection of proper superpixel generation techniques and superpixel image segmentation techniques play a very crucial role in the domain of different kinds of image segmentation. Clustering is a well-accepted image segmentation technique and proved their effective performance over various image segmentation field. Therefore, this study presents an up-to-date survey on the employment of superpixel image in combined with clustering techniques for the various image segmentation. The contribution of the survey has four parts namely (i) overview of superpixel image generation techniques, (ii) clustering techniques especially efficient partitional clustering techniques, their issues and overcoming strategies, (iii) Review of superpixel combined with clustering strategies exist in literature for various image segmentation, (iv) lastly, the comparative study among superpixel combined with partitional clustering techniques has been performed over oral pathology and leaf images to find out the efficacy of the combination of superpixel and partitional clustering approaches. Our evaluations and observation provide in-depth understanding of several superpixel generation strategies and how they apply to the partitional clustering method.

A Bayesian Fuzzy Clustering Approach for Design of Precipitation Gauge Network Using Merged Remote Sensing and Ground‐Based Precipitation Products

AbstractA two‐level clustering approach is proposed for optimal design/expansion of a ground‐based precipitation monitoring network (GPN). It harnesses the advantages of Infinite Bayesian fuzzy clustering in the first level to partition the study area into homogeneous precipitation zones by considering structural/statistical characteristics and temporal variability of the observed precipitation. In the second level, an ensemble of hierarchical and partitional clustering techniques is considered in the time domain to effectively partition each zone into groups by considering weighted inter‐site dissimilarities of precipitation. The dissimilarities account for correlation, temporal dynamics, and fuzzy mutual information of precipitation at existing stations and possible new gauge locations. Key station’s location in each group is identified by a proposed ranking procedure that accounts for population density, land‐use/landcover, and fuzzy marginal entropy of precipitation. For use with the approach, information on precipitation was derived for fine resolution ungauged grids covering the study area using random forest‐based regression relationships developed for gauged grids between merged multiple satellite‐based precipitation products (CHIRPS, IMERG) and ground‐based precipitation measurements. The potential of the proposed approach over other clustering‐based procedures is illustrated through a case study on a GPN comprising 1,128 gauges in Karnataka state (191,791 km2) of India. Potential locations for installing new gauges and areas where there is scope for relocating existing stations are identified. The proposed methodology appears promising and could be extended to design networks monitoring various other hydrometeorological variables.

3SHACC: Three stages hybrid agglomerative constrained clustering

Traditionally within the unsupervised learning paradigm, hierarchical and partitional clustering techniques have been shown to produce better results when provided with partial information, leading to a renewed attention towards this topic. Constrained clustering is a semi-supervised learning problem that combines classic clustering techniques with background knowledge given in the form of a set of constraints. In this paper, we propose to incorporate constraints into the clustering process in three phases: the first phase is devoted to quantify constraint relevance and to learn a metric matrix according to such relevance, a second phase computing similarities between instances by means of the reconstruction coefficient and pairwise distances, and a third stage performing agglomerative hierarchical clustering with a reward-style stepped affinity function favoring merges satisfying the higher possible number of constraints. Experimental results, supported by Bayesian statistical testing, show a consistent improvement in favor of our proposal over previous approaches to the constrained clustering problem.

A Cluster-Profile Comparative Study on Machining AlSi7/63% of SiC Hybrid Composite Using Agglomerative Hierarchical Clustering and K-Means

Clustering techniques are used to group the data based on the structure or through classification to reduce the mathematical complexity of large datasets. The hierarchical and partitioning approach are two broad clustering/classification techniques in data-mining. An attempt has been made to study the possibilities of taking the advantages of these approaches to machining. AlSi7/63% of SiC hybrid composite prepared by stir casting technique is machined using the Abrasive Water Jet Machine (AWJM) for Taguchi’s L27 Orthogonal Array (OA). Water Pressure, cutting distance, and cutting Speed are taken as independent parameters. Material Removal Rate (MRR), Kerf Angle (KA) and Surface profile Roughness (Ra) are taken as dependent responses. Support Vector Machine (SVM) classifiers with Agglomerative Hierarchical Clustering (AHC) classifies L27 OA into three classes of nine observations each. To compare and explore the difference between the partitional clustering and hierarchical clustering techniques at the same level of class, the study on K-means value is taken as 3 because of AHC group L27 OA into three classes. The value of K is fixed with three and it group into three classes of nine observations each. XLSTAT software is used for the analysis of AHC and K-means. Further, linear regression equations are developed for each class/classification of AHC and K-means and compared with the experimental observations. The analysis reveals that K-means classification based on the partitioning approach fits best with the experimental observations. AHC develops a single equation for all the classes, whereas K-means develops individual equations for all its classes.

Analytical review of clustering techniques and proximity measures

One of the most fundamental approaches to learn and understand from any type of data is by organizing it into meaningful groups (or clusters) and then analyzing them, which is a process known as cluster analysis. During this process of grouping, proximity measures play a significant role in deciding the similarity level of two objects. Moreover, before applying any learning algorithm on a dataset, different aspects related to preprocessing such as dealing with the sparsity of data, leveraging the correlation among features and normalizing the scales of different features are required to be considered. In this study, various proximity measures have been discussed and analyzed from the aforementioned aspects. In addition, a theoretical procedure for selecting a proximity measure for clustering purpose is proposed. This procedure can also be used in the process of designing a new proximity measure. Second, clustering algorithms of different categories have been overviewed and experimentally compared for various datasets of different domains. The datasets have been chosen in such a way that they range from a very low number of dimensions to a very high number of dimensions. Finally, the effect of using different proximity measures is analyzed in partitional and hierarchical clustering techniques based on experiments.

Hybrid Fuzzy Clustering Method Based on FCM and Enhanced Logarithmical PSO (ELPSO)

Fuzzy c-means (FCM) is one of the best-known clustering methods to organize the wide variety of datasets automatically and acquire accurate classification, but it has a tendency to fall into local minima. For overcoming these weaknesses, some methods that hybridize PSO and FCM for clustering have been proposed in the literature, and it is demonstrated that these hybrid methods have an improved accuracy over traditional partition clustering approaches, whereas PSO-based clustering methods have poor execution time in comparison to partitional clustering techniques, and the current PSO algorithms require tuning a range of parameters before they are able to find good solutions. Therefore, this paper introduces a hybrid method for fuzzy clustering, named FCM-ELPSO, which aim to deal with these shortcomings. It combines FCM with an improved version of PSO, called ELPSO, which adopts a new enhanced logarithmic inertia weight strategy to provide better balance between exploration and exploitation. This new hybrid method uses PBM(F) index and the objective function value as cluster validity indexes to evaluate the clustering effect. To verify the effectiveness of the algorithm, two types of experiments are performed, including PSO clustering and hybrid clustering. Experiments show that the proposed approach significantly improves convergence speed and the clustering effect.

Utilization of Summarization Algorithms for a Better Understanding of Clustered Medical Documents

Medical documents contain rich information about the diseases, medication, symptoms and precautions. Extraction of useful information from large volumes of medical documents that are generated by electronic health record systems is a complex task as they are unstructured or semi-structured. Various partitional and agglomerative clustering techniques are applied for grouping the medical documents into meaningful clusters [4]. Multi-document summarization techniques which are recent development in the field of Natural Language Processing are applied to condense the huge data present in the clustered medical documents to generate a single summary which conveys the key meaning. The summarization techniques can be broadly classified into two types [2]. They are: Extractive Summarization techniques and Abstractive Summarization techniques. Extractive Summarization techniques try to retrieve the most important sentences from the given document. Abstractive Summarization techniques try to generate summary with new sentences which are not present in the document. Extractive summarization techniques using Statistical Approaches are applied on the clustered medical documents. Medical summaries help the patients for a better and prior understanding of the disease and they can get a brief idea before consulting a physician. The generated summaries are evaluated using ROUGE (Recall Oriented Understudy of Gisting Evaluation) evaluation technique.

A Review of Computational Methods for Clustering Genes with Similar Biological Functions

Clustering techniques can group genes based on similarity in biological functions. However, the drawback of using clustering techniques is the inability to identify an optimal number of potential clusters beforehand. Several existing optimization techniques can address the issue. Besides, clustering validation can predict the possible number of potential clusters and hence increase the chances of identifying biologically informative genes. This paper reviews and provides examples of existing methods for clustering genes, optimization of the objective function, and clustering validation. Clustering techniques can be categorized into partitioning, hierarchical, grid-based, and density-based techniques. We also highlight the advantages and the disadvantages of each category. To optimize the objective function, here we introduce the swarm intelligence technique and compare the performances of other methods. Moreover, we discuss the differences of measurements between internal and external criteria to validate a cluster quality. We also investigate the performance of several clustering techniques by applying them on a leukemia dataset. The results show that grid-based clustering techniques provide better classification accuracy; however, partitioning clustering techniques are superior in identifying prognostic markers of leukemia. Therefore, this review suggests combining clustering techniques such as CLIQUE and k-means to yield high-quality gene clusters.

Analysis of low‐correlated spatial gene expression patterns: a clustering approach in the mouse brain data hosted in the Allen Brain Atlas

The Allen Brain Atlas (ABA) provides a similar gene expression dataset by genome‐scale mapping of the C57BL/6J mouse brain. In this study, the authors describe a method to extract the spatial information of gene expression patterns across a set of 1047 genes. The genes were chosen from among the 4104 genes having the lowest Pearson correlation coefficient used to compare the expression patterns across voxels in a single hemisphere for available coronal and sagittal volumes. The set of genes analysed in this study is the one discarded in the article by Bohland et al., which was considered to be of a lower consistency, not a reliable dataset. Following a normalisation task with a global and local approach, voxels were clustered using hierarchical and partitioning clustering techniques. Cluster analysis and a validation method based on entropy and purity were performed. They analyse the resulting clusters of the mouse brain for different number of groups and compared them with a classically‐defined anatomical reference atlas. The high degree of correspondence between clusters and anatomical regions highlights how gene expression patterns with a low Pearson correlation coefficient between sagittal and coronal sections can accurately identify different neuroanatomical regions.

Analysis of meteorological conditions in Spain by means of clustering techniques

A comprehensive analysis of clustering techniques is presented in this paper through their application to data on meteorological conditions. Six partitional and hierarchical clustering techniques (k-means, k-medoids, SOM k-means, Agglomerative Hierarchical Clustering, and Clustering based on Gaussian Mixture Models) with different distance criteria, together with some clustering evaluation measures (Calinski–Harabasz, Davies–Bouldin, Gap and Silhouette criterion clustering evaluation object), present various analyses of the main climatic zones in Spain. Real-life data sets, recorded by AEMET (Spanish Meteorological Agency) at four of its weather stations, are analyzed in order to characterize the actual weather conditions at each location. The clustering techniques process the data on some of the main daily meteorological variables collected at these stations over six years between 2004 and 2010.

DrugQuest - a text mining workflow for drug association discovery.

BackgroundText mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases.ResultsHerein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank “Description”, “Indication”, “Pharmacodynamics” and “Mechanism of Action” text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface.ConclusionsDrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest.

Segmentation-based classification of hyperspectral imagery using projected and correlation clustering techniques

A partitional clustering-based segmentation is used to carry out supervised classification for hyperspectral images. The main contribution of this study lies in the use of projected and correlation partitional clustering techniques to perform image segmentation. These types of clustering techniques have the capability to concurrently perform clustering and feature/band reduction, and are also able to identify different sets of relevant features for different clusters. Using these clustering techniques segmentation map is obtained, which is combined with the pixel-level support vector machines (SVM) classification result, using majority voting. Experiments are conducted over two hyperspectral images. Combination of pixel-level classification result with the segmentation maps leads to significant improvement of accuracies in both the images. Additionally, it is also observed that, classified maps obtained using SVM combined with projected and correlation clustering techniques results in higher accuracies as compared to classified maps obtained from SVM combined with other partitional clustering techniques.

Server Load – Balancing using Resources of Clustering Technique

Load Balancing is one of the most important issues for clustered servers. Load Balancing is done on the basis of server traffic. The proposed algorithm uses user-session history as the other constraint which is very useful for resource management over the server and easy access for the client. Collection of the data of a particular client from the last user-session, groups the applications that are likely to be opened in a specific time frame. This will provide easy access to the client and will reduce resource wastage on the server by using cluster technology. The primary clusters will be based on the bandwidth of the client’s internet bandwidth, which will map to different servers. Whereas secondary clusters will be based on the similarity of application-usage in the past user-session. This approach can reduce the unnecessary searching time. Cluster management will provide better approachability towards the server also the bandwidth based load balancing will lead to the minimum bandwidth wastage. Keywords Clustering, Load Balancing, Scalability, Client-Server, Resource Management, Cluster Creation, Cluster allocation, Cluster Reallocation, Cluster Management and Cloud Computing 1. INTRODUCTION The inspiration behind any Client-Server technology, arguably is its capacity to handle n no. of transactions in a given span of Time. Today, in the much credited dynamic environment “Scalability” is the concern for modern day Computer Scientist and Algorithm Pundits. You have only few ways left to deal with performance issues. 1. Either you are forced to upgrade hardware resources or 2. A load balancing [1] algorithm to find smart costefficient ways to fetch the user their desired resources without compromising Quality of Service. Coming directly to Load balancing on the Server, various methods have been used in past to balance the same, yet some haven’t got desired outcome. Clustering technique had been used in the near past for better fetch and efficient server loadmanagement Clustering Technique [2] is exploratory technique used in many areas to analyze large data sets. Given a proper notion of similarity, they find groups of similar variables/objects by partitioning the data set in “similar subsets”. Typically, several metrics over which a distance measure can be defined are associated with points (named samples) in the data set. There usually are various Clustering techniques available. Most used amongst them are I) Hierarchical and II) Partitioning clustering techniques. User Session Deduction is one of most important criteria for Load-Balancing, given the technique to be used is Server-Clusters. User-Session Identification has been non-trivial task to be performed on-the-go real time. Applications such as telnet or ssh typically generate a single TCP connection per single user-session, whereas application layer protocols such as HTTP, IMAP/SMTP and X11 usually generate multiple TCP connections per user-session. User-session characterization genuinely allows researchers to build realistic scenarios when assessing the performance of a complex network via simulation. Earlier Problems included I) Missed user Sessions, as threshold value had to be statically stated to tell the server that it is running over capacity. Vague, as it may sound, any usersession could be missed once there is bottleneck of connections asking permission for a connection establishment. II) Possibility of a huge data lose once there is a system failure since no backup image available, only to add to the chaos, a main server did not have any backup server available to route traffic towards that server. III) Every returning User had to start-over every time he/she terminates the session, it led to huge delay in page requested and page Fetched. As every user was forced to visit the prespecified pages and not the user-desired page. Our Approach will ensure a proper user session deduction takes place. The Algorithms will make sure to time stamp every usersession so that in case of any missing sessions the timestamp will auto-correct it’s reading. Apart from these a smart Artificial Intelligence Engine will predict user behavior with respect to their usage at different point of times. This will help user to directly visit his/ her required page rather than logging in again and getting diverted once again to Homepage. Also a smart cluster allocation Engine and cluster management algorithm will work hand in hand and deliver realtime cluster allocation to users without any hassles whatsoever. Even Cluster Allocation depends upon various parameters viz. Bandwidth, Predicted userbehavior. International Journal of Computer Applications (0975 – 8887) Volume 125 – No.5, September 2015 26 Fig 1: Architecture Diagram

Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization

Fuzzy clustering has become an important research field with many applications to real world problems. Among fuzzy clustering methods, fuzzy c-means (FCM) is one of the best known for its simplicity and efficiency, although it shows some weaknesses, particularly its tendency to fall into local minima. To tackle this shortcoming, many optimization-based fuzzy clustering methods have been proposed in the literature. Some of these methods are based solely on a metaheuristic optimization, such as particle swarm optimization (PSO) whereas others are hybrid methods that combine a metaheuristic with a traditional partitional clustering method such as FCM. It is demonstrated in the literature that methods that hybridize PSO and FCM for clustering have an improved accuracy over traditional partitional clustering approaches. On the other hand, PSO-based clustering methods have poor execution time in comparison to partitional clustering techniques. Another problem with PSO-based clustering is that the current PSO algorithms require tuning a range of parameters before they are able to find good solutions. In this paper we introduce two hybrid methods for fuzzy clustering that aim to deal with these shortcomings. The methods, referred to as FCM–IDPSO and FCM2–IDPSO, combine FCM with a recent version of PSO, the IDPSO, which adjusts PSO parameters dynamically during execution, aiming to provide better balance between exploration and exploitation, avoiding falling into local minima quickly and thereby obtaining better solutions. Experiments using two synthetic data sets and eight real-world data sets are reported and discussed. The experiments considered the proposed methods as well as some recent PSO-based fuzzy clustering methods. The results show that the methods introduced in this paper provide comparable or in many cases better solutions than the other methods considered in the comparison and were much faster than the other state of the art PSO-based methods.

Novel Approach for Network Traffic Pattern Analysis using Clustering-based Collective Anomaly Detection

There is increasing interest in the data mining and network management communities in improving existing techniques for the prompt analysis of underlying traffic patterns. Anomaly detection is one such technique for detecting abnormalities in many different domains, such as computer network intrusion, gene expression analysis, financial fraud detection and many more. Clustering is a useful unsupervised method for both identifying underlying patterns in data and anomaly detection. However, existing clustering-based techniques have high false alarm rates and consider only individual data instances for anomaly detection. Interestingly, there are traffic flows which seem legitimate but are targeted at disrupting a normal computing environment, such as the Denial of Service (DoS) attack. The presence of such anomalous data instances explains the poor performances of existing clustering-based anomaly detection techniques. In this paper, we formulate the problem of detecting DoS attacks as a collective anomaly which is a pattern in the data when a group of similar data instances behave anomalously with respect to the entire dataset. We propose a framework for collective anomaly detection using a partitional clustering technique to detect anomalies based on an empirical analysis of an attack’s characteristics. We validate our approach by comparing its results with those from existing techniques using benchmark datasets.

Comprehensive Study and Analysis of Partitional Data Clustering Techniques

Data clustering has found significant applications in various domains like bioinformatics, medical data, imaging, marketing study and crime analysis. There are several types of data clustering such as partitional, hierarchical, spectral, density-based, mixture-modeling to name a few. Among these, partitional clustering is well suited for most of the applications due to the less computational requirement. An analysis of various literatures available on partitional clustering will not only provide good knowledge, but will also lead to find the recent problems in partitional clustering domain. Accordingly, it is planned to do a comprehensive study with the literature of partitional data clustering techniques. In this paper, thirty three research articles have been taken for survey from the standard publishers from 2005 to 2013 under two different aspects namely the technical aspect and the application aspect. The technical aspect is further classified based on partitional clustering, constraint-based partitional clustering and evolutionary programming-based clustering techniques. Furthermore, an analysis is carried out, to find out the importance of the different approaches that can be adopted, so that any new development in partitional data clustering can be made easier to be carried out by researchers.

Distance Based Hybrid Approach for Cluster Analysis Using Variants of K-means and Evolutionary Algorithm

Clustering is a process of grouping same objects into a specified number of clusters. K-means and K-medoids algorithms are the most popular partitional clustering techniques for large data sets. However, they are sensitive to random selection of initial centroids and are fall into local optimal solution. K-means++ algorithm has good convergence rate than other algorithms. Distance metric is used to find the dissimilarity between objects. Euclidean distance metric is commonly used by number of researchers in most algorithms. In recent years, Evolutionary algorithms are the global optimization techniques for solving clustering problems. In this study, we present hybrid K-means++ with PSO technique (K++_PSO) clustering algorithm based on different distance metrics like City Block and Chebyshev. The algorithms are tested on four popular benchmark data sets from UCI machine learning repository and an artificial data set. The clustering results are evaluated through the fitness function values. We have made a comparative study of proposed algorithm with other algorithms. It has been found that K++_PSO algorithm using Chebyshev distance metric produces good clustering results as compared to other approaches.