• Publications
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RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets
  • P. Maji, S. Pal
  • Mathematics, Computer Science
  • Fundam. Informaticae
  • 1 December 2007
TLDR
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. Expand
Rough Set Based Generalized Fuzzy $C$ -Means Algorithm and Quantitative Indices
  • P. Maji, S. Pal
  • Mathematics, Computer Science
  • IEEE Transactions on Systems, Man, and…
  • 1 December 2007
TLDR
The RFPCM comprises a judicious integration of the principles of rough and fuzzy sets that incorporates both probabilistic and possibilistic memberships simultaneously to avoid the problems of noise sensitivity of fuzzy C-means and the coincident clusters of PCM. Expand
Rough-Fuzzy Clustering for Grouping Functionally Similar Genes from Microarray Data
  • P. Maji, Sushmita Paul
  • Medicine, Computer Science
  • IEEE/ACM Transactions on Computational Biology…
  • 1 March 2013
TLDR
An efficient method is proposed to select initial prototypes of different gene clusters, which enables the proposed c-means algorithm to converge to an optimum or near optimum solutions and helps to discover coexpressed gene clusters. Expand
Content-based image retrieval using visually significant point features
TLDR
This paper presents a new image retrieval scheme using visually significant point features extracted using a fuzzy set theoretic approach, which shows the robustness of the system is also shown when the images undergo different transformations. Expand
Rough-Fuzzy C-Medoids Algorithm and Selection of Bio-Basis for Amino Acid Sequence Analysis
In most pattern recognition algorithms, amino acids cannot be used directly as inputs since they are nonnumerical variables. They, therefore, need encoding prior to input. In this regard, bio-basisExpand
Evolving Cellular Automata as Pattern Classifier
TLDR
A high speed, low cost pattern classifier based on the sparse network of Cellular Automata is reported, demonstrating high quality of classification of patterns with or without noise. Expand
Rough set based maximum relevance-maximum significance criterion and Gene selection from microarray data
TLDR
A new feature selection algorithm is presented based on rough set theory that selects a set of genes from microarray data by maximizing the relevance and significance of the selected genes. Expand
Theory and Application of Cellular Automata For Pattern Classification
TLDR
Extensive experimental results demonstrate better performance of the proposed scheme over popular classification algorithms in respect of memory overhead and retrieval time with comparable classification accuracy. Expand
Maximum Class Separability for Rough-Fuzzy C-Means Based Brain MR Image Segmentation
TLDR
In this paper, the rough-fuzzy c -means (RFCM) algorithm is presented for segmentation of brain MR images and a comparison with other related algorithms is demonstrated on a set ofbrain MR images. Expand
Fuzzy Cellular Automata for Modeling Pattern Classifier
TLDR
Extensive experimental results confirm the scalability of the proposed FCA based classifier to handle large volume of datasets irrespective of the number of classes, tuples, and attributes. Expand
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