Abstract
Data Cluster analysis is an important part of data mining. It can be handled as two types, hard and soft clustering. In hard clustering, a dataset is divided into distinct clusters and each data in the dataset belongs to exactly one cluster. On the contrary data can belong to more than one cluster in soft clustering and each data can be associated with each cluster by a membership degree. Incremental algorithms which are developed for hard clustering have two main advantages. They based on the nonsmooth-nonconvex mathematical model which allows significantly reduce the number of variables and they choose one cluster center for each step that leads to obtain better objective function. In this paper, we propose an incremental fuzzy algorithm for soft clustering problems and present results of numerical experiments on 11 real-world datasets. These results demonstrate that the proposed algorithm is efficient for solving the soft clustering problems.
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