In this book, we first lay the groundwork by reviewing some standard clustering algorithms and projection algorithms before presenting various non-standard criteria for clustering. The family of algorithms developed are shown to perform better than the standard clustering algorithms on a variety of ...
A review of clustering concepts and algorithms is provided emphasizing: (a) output cluster structure, (b) input data kind, and (c) criterion. A dozen cluster structures is considered including those used in either supervised or unsupervised learning or both. The techniques discussed cover such ...
SSDR: An Algorithm for Clustering Categorical Data Using Rough Set Theory. In the present day scenario, there are large numbers of clustering algorithms available to group objects having similar characteristics. But the implementa... Tripathy,B.,K.,... - 《Advances in Applied Science Research》...
Clustering has primarily been used as an analytical technique to group unlabeled data for extracting meaningful information. The fact that no clustering algorithm can solve all clustering problems has resulted in the development of several clustering algorithms with diverse applications. We review data clu...
Clustering is a well-known task in Machine Learning that aims at grouping data into clusters according to their similarity. It has been long studied and many algorithms have been developed, among them the well-known K-means algorithm [54], [55] that aims at finding a partition of the objec...
Fuzzy clustering has been useful in capturing the uncertainty present in the data during clustering. Most of the c-Means algorithms such as FCM (F
Data analysis based on means, standard deviations, or clustering algorithms can reveal errors, whose values can sometimes be set to a mean or other statistical measure. b ) Imbalanced learning A sample with different proportions of positive and negative cases will lead to a bias in the learning...
survey may assist designers to choose the appropriate algorithms that suite intended applications. Furthermore, this work is expected to help newcomers to the field to determine the limitations associated with the available channel selection methods and to pave the road for the development of new ...
An increase in the size of data repositories of spatiotemporal data has opened up new challenges in the fields of spatiotemporal data analysis and data min
of the algorithm in various diversified applications, some modifications or hybridization with other algorithms are needed. A comprehensive survey on FCM and its applications in more than one decade has been carried out in this paper to show the efficiency and applicability in a mixture of domains....