文章基本信息

标题：Improving Cluster Formulation to Reduce Outliers in Data Mining
本地全文：下载
作者：Nancy Lekh ; Manish Mahajan
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2014
卷号：2
期号：6
出版社：S&S Publications
摘要：Existing studies in data mining focus on Outlier detection on data with single clustering algorithmmostly. There are lots of methods available in data mining to detect the outlier by making the clusters of data and thendetect the outlier from them .Where outlier is the data item whose value falls outside the bounds in the sample data mayindicate anomalous data. Outlier can be reduced if we improve the clustering .In this paper we proposed a hybridalgorithm that work not only on numeric data but also on text data. Our focus is to improve the cluster making so thatthe number of outliers can be reduce for that we can combine the clustering and classification techniques of datamining i.e. weighted k-mean and neural networks.
关键词：energy Data Mining; Outlier; Clustering; k-means; weighted k-mean; Neural Networks