Earticle

초록

영어

Clustering is a challenging task in data mining technique. The aim of clustering is to group the similar data into number of clusters. Various clustering algorithms have been developed to group data into clusters. However, these clustering algorithms work effectively either on pure numeric data or on pure categorical data, most of them perform poorly on mixed categorical and numerical data types in previous k-means algorithm was used but it is not accurate for large datasets. In this paper we cluster the mixed numeric and categorical data set in efficient manner. In this paper we present a clustering algorithm based on similarity weight and filter method paradigm that works well for data with mixed numeric and categorical features. We propose a modified description of cluster center to overcome the numeric data only limitation and provide a better characterization of clusters. The performance of this algorithm has been studied on benchmark data sets.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1 Cluster Ensemble Approach for Mixed Data
  2.2 Methodology
 3. Review of K-means Algorithm
  3.1 K-Means
  3.2 K-Prototype
 4. Proposed Algorithm
  4.1 Similarity Weight Method
  4.2 Clustering Similarity Analysis
  4.3. Filter Algorithm
  4.3 Advantages of Proposed System
 5. Clustering results
 6. Conclusion
 References

키워드

저자

• M. V. Jagannatha Reddy [ Department of CSE, Madanapalle Institute of Technology and Science Madanapalle ]
• B. Kavitha [ Department of MCA, Sree Vidyanikethan Engineering College, A.Rangampet ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605