Shape and Size Regularization in Expectation Maximization and Fuzzy Clustering
نویسندگان
چکیده
The more sophisticated fuzzy clustering algorithms, like the Gustafson–Kessel algorithm [11] and the fuzzy maximum likelihood estimation (FMLE) algorithm [10] offer the possibility of inducing clusters of ellipsoidal shape and different sizes. The same holds for the EM algorithm for a mixture of Gaussians. However, these additional degrees of freedom often reduce the robustness of the algorithm, thus sometimes rendering their application problematic. In this paper we suggest shape and size regularization methods that handle this problem effectively.
منابع مشابه
Fuzzy and Probabilistic Clustering with Shape and Size Constraints
More sophisticated fuzzy clustering algorithms, like the Gustafson–Kessel algorithm [11] and the fuzzy maximum likelihood estimation (FMLE) algorithm [10] offer the possibility of inducing clusters of ellipsoidal shape and different sizes. The same holds for the expectation maximization (EM) algorithm for a mixture of Gaussians. However, these additional degrees of freedom can reduce the robust...
متن کاملSample-weighted clustering methods
Keywords: Cluster analysis Maximum entropy principle k-means Fuzzy c-means Sample weights Robustness a b s t r a c t Although there have been many researches on cluster analysis considering feature (or variable) weights, little effort has been made regarding sample weights in clustering. In practice, not every sample in a data set has the same importance in cluster analysis. Therefore, it is in...
متن کاملParameter Estimation in Finite Mixture Models by Regularized Optimal Transport: A Unified Framework for Hard and Soft Clustering
In this short paper, we formulate parameter estimation for finite mixture models in the context of discrete optimal transportation with convex regularization. The proposed framework unifies hard and soft clustering methods for general mixture models. It also generalizes the celebrated k-means and expectation-maximization algorithms in relation to associated Bregman divergences when applied to e...
متن کاملHybrid Fuzzy C-Means Clustering Technique for Gene Expression Data
The challenging issue in microarray technique is to analyze and interpret the large volume of data. This can be achieved by clustering techniques in data mining. In hard clustering like hierarchical and k-means clustering techniques, data is divided into distinct clusters, where each data element belongs to exactly one cluster so that the out come of the clustering may not be correct in many ti...
متن کاملFUAT - A fuzzy clustering analysis tool
As it is known, fuzzy clustering is a kind of soft clustering method and primarily based on idea of segmenting data by using membership degrees of cases which are computed for each cluster. However, most of the current fuzzy clustering modules packaged in both open source and commercial products have lack of enabling users to explore fuzzy clusters deeply and visually in terms of investigation ...
متن کامل