NDLI: An Increased Performance of Clustering High Dimensional Data Using Principal Component Analysis

Content Provider	IEEE Xplore Digital Library
Author	Tajunisha, N. Saravanan, V.
Copyright Year	2010
Abstract	In many application domains such as information retrieval, computational biology, and image processing the data dimension is usually very high. Developing effective clustering methods for high dimensional dataset is a challenging problem due to the curse of dimensionality. The k-means clustering algorithm is used for many practical applications. But it is computationally expensive and the quality of the resulting clusters heavily depends on the selection of initial centroid and dimension of the data. The accuracy of the resultant value perhaps not up to the level of expectation when the dimensions of the dataset is high because we cannot say that the dataset chosen are free from noisy and flawless. So it is required to reduce the dimensionality of the given dataset in order to improve the efficiency and accuracy. This paper proposed a new approach to improve the accuracy of the cluster results by using PCA to determine the initial centroid and also to reduce the dimension of the data.
Starting Page	17
Ending Page	21
File Size	272583
Page Count	5
File Format	PDF
ISBN	9781424479634
DOI	10.1109/ICIIC.2010.31
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-05
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis K-means Iris Dimension reduction Accuracy Machine learning algorithms Clustering algorithms Partitioning algorithms Principal component analysis
Content Type	Text
Resource Type	Article

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