Robust k-Means Clustering and Fuzzy Principal Component Analysis
نویسندگان
چکیده
منابع مشابه
Principal Component Analysis and Effective K-Means Clustering
The widely adopted K-means clustering algorithm uses a sum of squared error objective function. A detailed analysis shows the close relationship between K-means clustering and principal component analysis (PCA) which is extensively utilized in unsupervised dimension reduction. We prove that the continuous solutions of the discrete K-means clustering membership indicators are the data projection...
متن کاملRobust and Sparse Fuzzy K-Means Clustering
The partition-based clustering algorithms, like KMeans and fuzzy K-Means, are most widely and successfully used in data mining in the past decades. In this paper, we present a robust and sparse fuzzy K-Means clustering algorithm, an extension to the standard fuzzy K-Means algorithm by incorporating a robust function, rather than the square data fitting term, to handle outliers. More importantly...
متن کاملFuzzy C-Means Clustering Based Principal Component Averaging Fusion
Image fusion is a method of imparting all relevant and complementary image details into a single composite image extracted from images of same source or various sources. This paper proposes a fusion method based on segmented regions of source images which are obtained by a fuzzy C-Means clustering algorithm. Robust clustering is exhibited by Fuzzy C-means algorithm by assigning fuzzy membership...
متن کاملRobust Local Principal Component Analyzer with Fuzzy Clustering
Non-linear extensions of Principal Component Analysis (PCA) have been developed for detecting the lower-dimensional representations of real world data sets and local linear approaches are used widely because of their computational simplicity and understandability. Fuzzy c-Varieties (FCV) is the linear fuzzy clustering algorithm that estimates local principal component vectors as the vectors spa...
متن کاملSnipping for robust k-means clustering under component-wise contamination
We introduce the concept of snipping, complementing that of trimming, in robust cluster analysis. An observation is snipped when some of its dimensions are discarded, but the remaining are used for clustering and estimation. Snipped k-means is performed through a probabilistic optimization algorithm which is guaranteed to converge to the global optimum. We show global robustness properties of o...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Japan Society for Fuzzy Theory and Intelligent Informatics
سال: 2013
ISSN: 1347-7986,1881-7203
DOI: 10.3156/jsoft.25.3_74