Earticle

초록

영어

K-Means is a famous partition based clustering algorithm. Various extensions of K-Means have been proposed depending on the type of datasets being handled. Popular ones include K-Modes for categorical data and K-Prototype for mixed numerical and categorical data. The K-Means and its extensions suffer from one major limitation that is dependency on prior input of number of clusters K. Sometimes it becomes practically impossible to correctly estimate the optimum number of clusters in advance. Various ways have been suggested in literature to overcome this limitation for numerical data. But for categorical and mixed data work is still in progress. In this paper, we introduce a new algorithm based on the K-Means that takes mixed dataset as an input and generates appropriate number of clusters on the run using MapReduce programming style. The new algorithm not only overcomes the limitation of providing the value of K initially but also reduces the computation time using MapReduce framework.

목차

Abstract
 1. Introduction
 2. An Extended K-Means Algorithm
  2.1. The Pseudocode of the Extended K-Means Algorithm
 3. An Extended K-Means Algorithm using MapReduce Framework
 4. Psuedocode of the Extended K-Means Algorithm using MapReduce Framework
 5. Illustrative Example
 6. Conclusion and Future Work
 References

키워드

저자

• Anupama Chadha [ Faculty of Computer Applications, MRIU, Faridabad, India ]
• Suresh Kumar [ Faculty of Engineering and Technology, MRIU, Faridabad, India ]

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