Analysis of SimpleKMeans with Multiple Dimensions using WEKA

Abstract

Clustering techniques have more importance in data mining especially when the data size is very large. It is widely used in the fields including pattern recognition system, machine learning algorithms, analysis of images, information retrieval and bio-informatics. Different clustering algorithms are available such as Expectation Maximization (EM), Cobweb, FarthestFirst, OPTICS, SimpleKMeans etc. SimpleKMeans clustering is a simple clustering algorithm. It partitions n data tuples into k groups such that each entity in the cluster has nearest mean. This paper is about the implementation of the clustering techniques using WEKA interface. This paper includes a detailed analysis of various clustering techniques with the different standard online data sets. Analysis is based on the multiple dimensions which include time to build the model, number of attributes, number of iterations, number of clusters and error rate. General TermsData mining, Clustering, WEKA interface.

Predicting Global Energy Consumption Through Data Mining Techniques

“…The Euclidean distance is the unit of measurement used by the K-Means method to determine the distance between an object and the centroid. The process goes on until there is no net change left [43][44][45][46]. Figure 6 presents the clustering plot using a simple K-Means algorithm.…”

Performance Evaluation of Weka Clustering Algorithms on Large Datasets.

“…Sapna Jain et al [12] have evaluated the performance of the K-Means clustering using Weka tool. Rupali Patil et al [13] have also evaluated the performance of K-Means Clustering algorithm for multiple dimensions using Weka tool. The various dimensions considered are Time taken to build the model, number of attributes, number of iterations, number of clusters and error rate.…”