Graded Possibilistic Clustering of Non-stationary Data Streams
نویسندگان
چکیده
Multidimensional data streams are a major paradigm in data science. This work focuses on possibilistic clustering algorithms as means to perform clustering of multidimensional streaming data. The proposed approach exploits fuzzy outlier analysis to provide good learning and tracking abilities in both concept shift and concept drift.
منابع مشابه
An Algorithm to Model Paradigm Shifting in Fuzzy Clustering
The graded possibilistic clustering paradigm includes as the two extreme cases the “probabilistic” assumption and the “possibilistic” assumption adopted by many clustering algorithms. We propose an implementation of a graded possibilistic clustering algorithm based on an interval equality constraint enforcing both the normality condition and the required graded possibilistic condition. Experime...
متن کاملSeveral Formulations for Graded Possibilistic Approach to Fuzzy Clustering
Fuzzy clustering is a useful tool for capturing intrinsic structure of data sets. This paper proposes several formulations for soft transition of fuzzy memberships from probabilistic partition to possibilistic one. In the proposed techniques, the free memberships are given by introducing additional penalty term used in Possibilistic c-Means. The new features of the proposed techniques are demon...
متن کاملImage Segmentation: Type–2 Fuzzy Possibilistic C-Mean Clustering Approach
Image segmentation is an essential issue in image description and classification. Currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. Moreover, there are many uncertainties and vagueness in images, which crisp clustering and even Type-1 fuzzy clustering could not handle. Hence, Type-...
متن کاملPossibilistic Clustering in Feature Space
In this paper we propose the Possibilistic C-Means in Feature Space and the One-Cluster Possibilistic C-Means in Feature Space algorithms which are kernel methods for clustering in feature space based on the possibilistic approach to clustering. The proposed algorithms retain the properties of the possibilistic clustering, working as density estimators in feature space and showing high robustne...
متن کاملIntroduction to stream: An Extensible Framework for Data Stream Clustering Research with R
In recent years, data streams have become an increasingly important area of research for the computer science, database and statistics communities. Data streams are ordered and potentially unbounded sequences of data points created by a typically non-stationary data generating process. Common data mining tasks associated with data streams include clustering, classification and frequent pattern ...
متن کامل