Proceedings ArticleDOI
A decision theoretic rough fuzzy c-means algorithm
Sresht Agrawal,B. K. Tripathy +1 more
- pp 192-196
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TLDR
A clustering algorithm using DTRS and fuzzy sets in view called the decision-theoretic rough fuzzy c-means (DTRFCM) is developed and experiments show that this approach is more efficient than the D TRS algorithm.Abstract:
Imprecision based data clustering algorithms have gained a lot of importance these days because of the imprecise character of modern day databases. Some such algorithms are the rough c-means (RCM), fuzzy c-means (FCM) and their hybrid versions. Li et al used the decision theoretic rough set (DTRS) model by the way improving the RCM. In their approach they have used a notion called loss function to limit the information lost due to neighbours. The method of allocation using decision-theoretic rough sets model deals with potentially high computational cost. It has been observed that hybrid models are better than individual models. Keeping this in view, here we develop a clustering algorithm using DTRS and fuzzy sets in view called the decision-theoretic rough fuzzy c-means (DTRFCM). Experiments carried out show that our approach is more efficient than the DTRS algorithm. For this purpose we used several well-known data sets and parameters like the DB-index, D-index and Accuracy measure.read more
Citations
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Book ChapterDOI
Rough Kernelized Fuzzy C-Means Based Medical Image Segmentation
Amiya Halder,Siddhartha Guha +1 more
TL;DR: A rough kernelized fuzzy c-means clustering (RKFCM) based medical image segmentation algorithm that uses rough set with KFCM for removal of uncertainty by introduction of higher and lower estimation of rough set theory.
Book ChapterDOI
An Analysis of Decision Theoretic Kernalized Rough C -Means
TL;DR: This work has selected three of the most popular kernels and developed an improved Kernelized rough c-means algorithm that can be used for data clustering and compares the results with the basic decision theoretic rough c -means.
References
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Book ChapterDOI
Decision-theoretic rough set models
TL;DR: It is shown that the decision-theoretic models need to consider additional issues in probabilistic rough set models.
Journal ArticleDOI
Rough–Fuzzy Collaborative Clustering
TL;DR: A novel clustering architecture is introduced, in which several subsets of patterns can be processed together with an objective of finding a common structure, and the required communication links are established at the level of cluster prototypes and partition matrices.
Journal ArticleDOI
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives
TL;DR: This article compares k-mean to fuzzy c-means and rough k-Means as important representatives of soft clustering, and surveys important extensions and derivatives of these algorithms.
Journal ArticleDOI
RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets
Pradipta Maji,Sankar K. Pal +1 more
TL;DR: A hybrid unsupervised learning algorithm, termed as rough-fuzzy c-means, is proposed, which comprises a judicious integration of the principles of rough sets and fuzzy sets and which enables efficient handling of overlapping partitions.
Journal ArticleDOI
Decision-theoretic rough set
TL;DR: A DTRS by a set of cost matrices is proposed, from the viewpoint of the voting fusion mechanism, a parameterized decision-theoretic rough set is proposed and the smallest possible cost and the largest possible cost are calculated in decision systems.