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A Review of Data Clustering Techniques and Enhancement of Data Clustering using Hybrid Clustering Model of K‐Means and PSO Clustering Vinod Sharma, Nitish Salwan, Sandeep Singh, Navneet Singh Babra & Prabhsimran Singh E-mail : [email protected], [email protected], [email protected],

Aug 20, 2017· K-Means is a clustering algorithm which is used for cluster analysis in data mining; it partitions the data set into k clusters. In this project, K-Means algorithm is optimized using PSO (Parm Swarm Optimization)in terms of time. PSO simulates the social behavior of birds and helps to improve candidate solution iteratively. This project is made in python and has been tested on some standard ...

able to scale with increasing data set sizes. In this paper, we propose a parallel particle swarm optimization clustering (MR-CPSO) algorithm that is based on MapReduce. The experimental results reveal that MR-CPSO scales very well with increasing data set sizes and achieves a very close to the linear speedup while maintaining the clustering ...

Bhise R.B, Thorat S.S and Supekar A.K. (2013) [14] used data mining process in a student's database using K-means clustering algorithm to predict students result. Varun Kumar and Anupama Chadha (2013) [15] used of one of the data mining technique called association rule mining in enhancing the quality of students' performances at

Comparative Study of Particle Swarm Optimization based Unsupervised Clustering Techniques Mr.V.K.Panchal1, ... find the "optimum" number of clusters in a data set • To show that PSO can bring out results with in ... Fuzzy K-means with extragrades. Data …

educational data mining is a promising area of research and it has a specific requirements not presented in other domains. Thus, work should be oriented towards educational domain of data mining. El-Halees [5], gave a case study that used educational data mining to analyze students' learning behavior. The goal of

applied on various datasets from UCI repository. The experimental results of this paper show that PSO-K-means and PCA-K-Means improves the performance of basic K-means in terms of accuracy and computational time. Keywords : Clustering, PSO, PSO-K-means, QPSO 1. Introduction Data clustering is a technique in which data with similar

PSO+K-means Algorithm for Anomaly Detection in Big Data The use of clustering methods in anomaly detection is considered as an effective approach. The choice of the cluster primary center and the finding of local optimum in the well-known k-means and other classic clustering algorithms are considered as one of the major problems and do not ...

A Higher Education Predictive Model Using Data Mining Techniques Subhalaxmi Panda Department of Computer Science & ... K-Means, Naïve Bayes, Random Forest algorithm. In this ... educational data mining." International Journal of Information and Electronics Engineering, Volume 5, Issues.2, ...

Keywords: Data mining; K-means clustering MAFIA (Maximal Frequent Item set Algorithm; C4.5 algorithm. 1. Introduction Data mining is process of extracting useful information from large amount of databases. Data mining is most useful in an exploratory analysis because of nontrivial information in large volumes of data.

A New Improved Hybridized K-MEANS Clustering Algorithm with Improved PCA Optimized with PSO for High Dimensional Data Set . ... K-means clustering algorithm is an efficient clustering algorithm to cluster the data, but the problem with the k-means is that when the dimension of the data set becomes larger the effectiveness of k-means is lost ...

Datasets for Data Mining and Data Science. See also Government, State, City, Local, public data sites and portals ... a home equity loans credit data set, mortgage loan level data set, Loss Given Default ... Data.gov/Education, central guide for education data resources including high-value data sets, ...

A NEW HYBRID ALGORITHM BASED ON PSO, SA, AND K-MEANS FOR CLUSTER ANALYSIS ... evaluated by several benchmark data sets. The simulation results show that the proposed ... previous approaches such as PSO, SA, combination of PSO and SA (PSO-SA), Ant Colony Optimization (ACO), combination of k-means and PSO (K-PSO), combination of Nelder Mead ...

analyzed using a data mining technique namely k-means clustering. The data set used in this study was obtained from department of Bachelor of computer Application (B.C.A.), N.M.C College, Trichy, in Nov-2013.The programming environment use for application was Visual studio 2008 for building data mining model it was

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Dec 12, 2003· Data clustering using particle swarm optimization ... This second algorithm basically uses PSO to refine the clusters formed by K-means. The new PSO algorithms are evaluated on six data sets, and compared to the performance of K-means clustering. Results show that both PSO clustering techniques have much potential.

Developing the Code: Executing Particle Swarm Optimization in SAS® Anurag Srivastava Sangita Kumbharvadiya. ABSTRACT Particle swarm optimization is a heuristic global optimization method which was given by James Kennedy and Russell C. Eberhart in 1995. ... Intelligence in Data Mining) K – means algorithm depends on

Clustering Multidimensional Data with PSO based Algorithm Jayshree Ghorpade-Aher and Vishakha A. Metre Abstract: Data clustering is a recognized data analysis method in data mining whereas K-Means is the well known partitional clustering method, possessing pleasant features.

A hybrid sequential approach for data clustering using K-Means and particle swarm optimization algorithm ... K-Means clustering, Particle Swarm Optimization (PSO) ... a data set into k groups. K ...

This study presents a new clustering algorithm, particle swarm optimization with the focal particles (PSOFP). Contrary to the standard particle swarm optimization (PSO) approach, this new clustering technique ensures high quality clustering results without increasing the dimensions of the search space.

based on k-means such that the data is partitioned into K clusters. However, the k-means algorithm highly depends on ... Data Set A data set is a collection of data. Input data is an ... Particle Swarm Optimization), ACO (Ant Colony Optimization) and K-means algorithm, which can find the cluster partition. After choosing the cluster centers, K-

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters).It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition ...

The new PSO-ACO algorithm is tested on several data sets, and its performance is compared with those of ACO, PSO and K-means clustering. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handing data clustering.

often implements aspects of the educational data mining (EDM) theory, which aims to study available data in order to shed light on more valuable, hidden information. Data clustering, classification, and regression methods such as K-means clustering, neural networks (NN), extreme learning machine (ELM), and support vector machines (SVM) can

set and it is extractable through data mining techniques. Present paper is designed to justify the capabilities of data mining techniques in context of higher education by offering a data mining model for higher education system in the university. In this research, the classification task is …

Key-Words: - Particle Swarm Optimization (PSO); Entropy-based Fuzzy Clustering; Cluster Center Initialization 1 Introduction As an important method in the field of data mining, Clustering is the process of partitioning dataset with n data points into many sub-sets. Each sub-set represents one cluster and the data points in the

Fuzzy c-means clustering algorithm (FCM) [1-2] is an effective algorithm and is one of the most used clustering methods. But when the data set has a higher dimension, the clustering effect of FCM is poor, and it is difficult to find the global optimum [3-4]. Particle Swarm Optimization (PSO) [11] is one of the

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