Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Swagatam Das"'
Publikováno v:
IEEE Transactions on Fuzzy Systems. 27:1226-1236
The fuzzy $k$ -nearest neighbor classifier (F $k$ NN) improves upon the flexibility of the $k$ -nearest neighbor classifier by considering each class as a fuzzy set and estimating the membership of an unlabeled data instance for each of the classes.
Autor:
Swagatam Das, Arkajyoti Saha
Publikováno v:
Fuzzy Sets and Systems. 340:1-37
Fuzzy data occurs frequently in the fields of decision making, social sciences, and control theory. We consider the problem of clustering fuzzy data along with automatic component number detection and feature selection. A model selection criterion ca
Publikováno v:
Pattern Recognition Letters. 101:80-87
Unlike the canonical k-Nearest Neighbor classifier (kNN) which treat the neighbors equally, the Fuzzy k-Nearest Neighbor (FkNN) classifier imposes a weight on each of the k nearest neighbors based on their distances from the query point, by using a f
Autor:
Arkajyoti Saha, Swagatam Das
Publikováno v:
Information Sciences. 408:129-145
For decades practitioners have been using the center-based partitional clustering algorithms like Fuzzy C Means (FCM), which rely on minimizing an objective function, comprising of an appropriately weighted sum of distances of each data point from th
Autor:
Arkajyoti Saha, Swagatam Das
Publikováno v:
Machine Learning. 106:951-992
The performance of a clustering algorithm can be improved by assigning appropriate weights to different features of the data. Such feature weighting is likely to reduce the effect of noise and irrelevant features while enhancing the effect of the dis
Autor:
Swagatam Das, Arkajyoti Saha
Publikováno v:
Information Sciences. 372:796-814
Dissimilarity measures play a key role in exploring the inherent cluster structure of the data for any partitional clustering algorithm. Commonly used dissimilarity functions for clustering purpose are so far confined to the Euclidean, exponential an
Autor:
Swagatam Das, Arkajyoti Saha
Publikováno v:
Pattern Recognition Letters. 79:60-67
Development of FCM with geometric divergence measure.Investigating sub-optimization problems constituting the Alternating Optimization.Performance comparison with FCM and weighted FCM.Detailed theoretical proof of strong resilience to noise features.
Autor:
Arkajyoti Saha, Swagatam Das
Publikováno v:
Pattern Recognition Letters. 63:50-58
The feature weighting schemes for crisp and fuzzy clustering are generalized.Alternating optimization heuristics are derived for hard and fuzzy clustering.Existence of the minimizer of the proposed optimization tasks is proved.Prominent weighting sch
Autor:
Swagatam Das, Arkajyoti Saha
Publikováno v:
Neurocomputing. 166:422-435
This article presents and investigates a new variant of the fuzzy k-Modes clustering algorithm for categorical data with automated feature weight learning. The modification strengthens the classical fuzzy k-Modes algorithm by associating higher weigh
Autor:
Avisek Gupta, Swagatam Das
Publikováno v:
ICAPR
This paper presents a common algorithm for the kernel k-harmonic means (KKHM) and the kernel fuzzy c-means (KFCM) clustering problems. We incorporate kernel functions in a generalized fuzzy c- means cost function, forming the cost function of a kerne