NDLI: An Enhanced Clustering Method Based on Grid-Shaking

Content Provider	IEEE Xplore Digital Library
Author	Jinbeom Kang Joongmin Choi Jaeyoung Yang
Copyright Year	2009
Abstract	Clustering is an essential way to extract meaningful information from massive data without human intervention in the field of data mining. Clustering algorithms can be divided into four types: partitioning algorithms, hierarchical algorithms, grid-based algorithms, and locality-based algorithms. Each algorithm, however, has problems that are not easily solved. K-means, for example, suffer from setting up an initial centroid problem when distribution of data is not hyper-ellipsoid. Chain effect, outlier, and degree of density in data are problems occurring in other types of algorithms. To solve these problems, various kinds of algorithms were proposed. In this paper, we propose a novel grid-based clustering algorithm through building clusters in each cell and show how to solve the previously mentioned problems.
Starting Page	673
Ending Page	678
File Size	740917
Page Count	6
File Format	PDF
ISBN	9781424439997
DOI	10.1109/WAINA.2009.100
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-26
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	chain effect Data analysis Shape Clustering methods Humans outlier Partitioning algorithms Data mining Application software Grid-Shaking Computer science Clustering algorithms k-means Density functional theory clustering
Content Type	Text
Resource Type	Article

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