NDLI: A Simple and Fast Algorithm for Global K-means Clustering

Content Provider	IEEE Xplore Digital Library
Author	Juanying Xie Shuai Jiang
Copyright Year	2010
Abstract	K-means clustering is a popular clustering algorithm based on the partition of data. However, there are some shortcomings of it, such as its requiring a user to give out the number of clusters at first, and its sensitiveness to initial conditions, and its easily getting to the trap of a local solution et cetera. The global K-means algorithm proposed by Likas et al is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N (with N being the size of the data set) runs of the K-means algorithm from suitable initial positions. It avoids the depending on any initial conditions or parameters, and considerably outperforms the K-means algorithms, but it has a heavy computational load. In this paper, we propose a new version of the global K-means algorithm. The outstanding feature of our algorithm is its superiority in execution time. It takes less run time than that of the available global K-means algorithms. This great advantage is due to that we improved the way of creating the next cluster center in the global K-means algorithm. We defined a novel function to select the optimal candidate center for the next cluster enlightened by the idea of K-medoids clustering algorithm suggested by Park and Jun. Experiments on some well-known data sets from UCI and on a synthetic data set show that our new algorithm can significantly reduce the computational time without affecting the performance of the global K-means algorithm. The further experiments demonstrate that our improved algorithm outperforms the global K-means algorithm greatly.
Starting Page	36
Ending Page	40
File Size	253645
Page Count	5
File Format	PDF
ISBN	9781424463886
e-ISBN	9781424463893
DOI	10.1109/ETCS.2010.347
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning algorithms Optimization methods data mining Educational technology Partitioning algorithms Pattern recognition Data mining machine learning K-means clustering Computer science Clustering algorithms Machine learning non-smooth optimization clustering global K-means clustering Computer science education pattern recognition
Content Type	Text
Resource Type	Article

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