The Experts below are selected from a list of 165366 Experts worldwide ranked by ideXlab platform
Anil Kumar Gupta - One of the best experts on this subject based on the ideXlab platform.
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A Comparative study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithm
International Journal of Computer Trends and Technology, 2014Co-Authors: Dibya Jyoti Bora, Anil Kumar GuptaAbstract:Data Clustering is an important area of data mining. This is an unsupervised study where data of similar types are put into one cluster while data of another types are put into different cluster. Fuzzy C means is a very important Clustering technique based on fuzzy logic. Also we have some hard Clustering techniques available like K-means among the popular ones. In this paper a comparative study is done between Fuzzy Clustering Algorithm and hard Clustering Algorithm.
Mohammad Javadian - One of the best experts on this subject based on the ideXlab platform.
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a novel density based fuzzy Clustering Algorithm for low dimensional feature space
Fuzzy Sets and Systems, 2017Co-Authors: Mohammad Javadian, Saeed Bagheri Shouraki, Soroush Sheikhpour KourabbaslouAbstract:Abstract In this paper, we propose a novel density-based fuzzy Clustering Algorithm based on Active Learning Method (ALM), which is a methodology of soft computing inspired by some hypotheses claiming that human brain interprets information in pattern-like images rather than numerical quantities. The proposed Clustering Algorithm, Fuzzy Unsupervised Active Learning Method (FUALM), is performed in two main phases. First, each data point spreads in the feature space just like an ink drop that spreads on a sheet of paper. As a result of this process, densely connected ink patterns are formed that represent clusters. In the second phase, a fuzzifying process is applied in order to summarize the effects of all members of each cluster. Finding arbitrary shaped clusters, noise robustness and proposing fuzzy clusters are some of the advantages of our proposed Clustering Algorithm. The Algorithm is described in full details and its performance is evaluated and compared with well-known Clustering Algorithms on synthetic and real-world datasets.
Jorg Sander - One of the best experts on this subject based on the ideXlab platform.
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a distribution based Clustering Algorithm for mining in large spatial databases
International Conference on Data Engineering, 1998Co-Authors: Xiaowei Xu, Martin Ester, Hanspeter Kriegel, Jorg SanderAbstract:The problem of detecting clusters of points belonging to a spatial point process arises in many applications. In this paper, we introduce the new Clustering Algorithm DBCLASD (Distribution-Based Clustering of LArge Spatial Databases) to discover clusters of this type. The results of experiments demonstrate that DBCLASD, contrary to partitioning Algorithms such as CLARANS (Clustering Large Applications based on RANdomized Search), discovers clusters of arbitrary shape. Furthermore, DBCLASD does not require any input parameters, in contrast to the Clustering Algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) requiring two input parameters, which may be difficult to provide for large databases. In terms of efficiency, DBCLASD is between CLARANS and DBSCAN, close to DBSCAN. Thus, the efficiency of DBCLASD on large spatial databases is very attractive when considering its nonparametric nature and its good quality for clusters of arbitrary shape.
Dibya Jyoti Bora - One of the best experts on this subject based on the ideXlab platform.
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A Comparative study Between Fuzzy Clustering Algorithm and Hard Clustering Algorithm
International Journal of Computer Trends and Technology, 2014Co-Authors: Dibya Jyoti Bora, Anil Kumar GuptaAbstract:Data Clustering is an important area of data mining. This is an unsupervised study where data of similar types are put into one cluster while data of another types are put into different cluster. Fuzzy C means is a very important Clustering technique based on fuzzy logic. Also we have some hard Clustering techniques available like K-means among the popular ones. In this paper a comparative study is done between Fuzzy Clustering Algorithm and hard Clustering Algorithm.
Mu Yong - One of the best experts on this subject based on the ideXlab platform.
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On Convergence of the Maximum Entropy Clustering Algorithm
Journal of Northern Jiaotong University, 2003Co-Authors: Mu YongAbstract:In this paper, we study the maximum entropy Clustering Algorithm and its converged points set, and prove the convergence theorem of the maximum entropy Clustering Algorithm again. Our study shows that the maximum entropy Clustering Algorithm may not converge to local minimum, sometimes converges to saddle points. Moreover, we obviously give the criterion of the judging whether the maximum entropy Clustering Algorithm converge to local minimum or not.
