Fuzzy K-means clustering algorithms for interval-valued data based on adaptive quadratic distances

2010 ◽  
Vol 161 (23) ◽  
pp. 2978-2999 ◽  
Author(s):  
Francisco de A.T. de Carvalho ◽  
Camilo P. Tenório
Keyword(s):  
Clustering Algorithms ◽  
Interval Valued

A Study of FCM Clustering Algorithm Based on Interval Multiple Attribute Information

2013 ◽  
Vol 444-445 ◽  
pp. 676-680
Author(s):  
Li Guo ◽  
Guo Feng Liu ◽  
Yu E Bao
Keyword(s):  
Clustering Algorithm ◽  
Distance Measure ◽  
Clustering Algorithms ◽  
Interval Numbers ◽  
Fuzzy Partition ◽  
Multiple Attribute ◽  
Attribute Information ◽  
Fcm Clustering ◽  
Attribute Clustering ◽  
Interval Valued

In multiple attribute clustering algorithms with uncertain interval numbers, most of the distances between the interval-valued vectors only consider the differences of each interval endpoint ignoring a lot of information. To solve this problem, according to the differences between corresponding points in each interval number, this paper gives a distance formula between interval-valued vectors, extends a FCM clustering algorithm based on interval multiple attribute information. Through an example, we prove the validity and rationality of the algorithm. Keywords: interval-valued vector; FCM clustering algorithm; distance measure; fuzzy partition

Variable-Wise Kernel-Based Clustering Algorithms for Interval-Valued Data

2012 ◽  
Author(s):  
Francisco de_A.T. de Carvalho ◽  
Gibson B.N. Barbosa ◽  
Marcelo R.P. Ferreira
Keyword(s):  
Clustering Algorithms ◽  
Interval Valued

Towards interval-valued fuzzy set-based collaborative fuzzy clustering algorithms

2018 ◽  
Vol 81 ◽  
pp. 404-416 ◽  
Author(s):  
Long Thanh Ngo ◽  
Trong Hop Dang ◽  
Witold Pedrycz
Keyword(s):  
Fuzzy Clustering ◽  
Fuzzy Set ◽  
Clustering Algorithms ◽  
Interval Valued

scholarly journals Intuitionistic Fuzzy Possibilistic C Means Clustering Algorithms

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Arindam Chaudhuri
Keyword(s):  
Fuzzy Sets ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Intuitionistic Fuzzy Sets ◽  
Distance Measures ◽  
Mathematical Framework ◽  
Cluster Validity ◽  
Intuitionistic Fuzzy ◽  
Validity Measures ◽  
Interval Valued

Intuitionistic fuzzy sets (IFSs) provide mathematical framework based on fuzzy sets to describe vagueness in data. It finds interesting and promising applications in different domains. Here, we develop an intuitionistic fuzzy possibilistic C means (IFPCM) algorithm to cluster IFSs by hybridizing concepts of FPCM, IFSs, and distance measures. IFPCM resolves inherent problems encountered with information regarding membership values of objects to each cluster by generalizing membership and nonmembership with hesitancy degree. The algorithm is extended for clustering interval valued intuitionistic fuzzy sets (IVIFSs) leading to interval valued intuitionistic fuzzy possibilistic C means (IVIFPCM). The clustering algorithm has membership and nonmembership degrees as intervals. Information regarding membership and typicality degrees of samples to all clusters is given by algorithm. The experiments are performed on both real and simulated datasets. It generates valuable information and produces overlapped clusters with different membership degrees. It takes into account inherent uncertainty in information captured by IFSs. Some advantages of algorithms are simplicity, flexibility, and low computational complexity. The algorithm is evaluated through cluster validity measures. The clustering accuracy of algorithm is investigated by classification datasets with labeled patterns. The algorithm maintains appreciable performance compared to other methods in terms of pureness ratio.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

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