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With the rapid development of uncertain and large-scale datasets, Fuzzy Possibilistic C-means Clustering (FPCM) and Granular Computing (GrC) were introduced together with the aim to solve the feature selection and outlier detection problems. Utilizing the advantages of the FPCM and GrC, an Advanced Fuzzy Possibilistic C-means Clustering based on Granular Computing (GrFPCM) was proposed to select features...
Word Classification involves grouping the words in a document into clusters. Clustering data sets is a much researched problem. In 2005, Nikhil R. Pal, Kuhu Pal, James M. Keller, and James C. Bezdek proposed A Possibilistic Fuzzy c-Means (PFCM) Clustering Algorithm. The PFCM model gives the membership values and the typicality values, along with the cluster centers. It is a hybrid algorithm of possibilistic...
SenticNet 1.0 is one of the most widely used freely-available resources for concept-level opinion mining, containing about 5,700 common sense concepts and their corresponding polarity scores. Specific affective information associated to such concepts, however, is often desirable for tasks such as emotion recognition. In this work, we propose a method for assigning emotion labels to SenticNet concepts...
While cloud computing continues to advance as a technology, its security is still perceived as a major concern in the corporate world. Several approaches are being considered in order to resolve the security issues so that the maximum potential of cloud computing can be exploited. Cloud forensics is one such approach that attempts to provide the requisite tools for investigation and analysis of cloud...
A web crawler is a relatively simple automated program or script that methodically scans or “crawls” through Internet pages to retrieval information from data. Alternative names for a web crawler include web spider, web robot, bot, crawler, and automatic indexer. There are many different uses for a web crawler. Their primary purpose is to collect data so that when Internet surfers enter a search term...
This paper presents a framework, based on Petri net and dynamic fuzzy clustering, to describe and to schedule task workflows in Grid environments. Computational Grids are intrinsically heterogeneous and dynamic systems, these features make difficult to build a resource scheduler that update itself automatically, following the resource evolution. In order to build a Grid scheduling system that automatically...
Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. However FCM is sensitive to initialization and is easily trapped in local optima. Particle swarm optimization (PSO) is a stochastic global...
Clustering Web session is an important aspect of Web usage mining. In this paper, we propose a new algorithm of Web session fuzzy clustering, which applies the t-bridge algorithm to fuzzy equivalence matrix clustering algorithm. This algorithm is proved to have better accuracy, fewer CPU time and better scalability than others by the experiments.
This paper studies the problem of weighting and selecting attributes and principal axes in fuzzy clustering. Its main contribution is a selection method that is not based on simply applying a threshold to computed feature weights, but directly assigns zero weights to features that are not informative enough. This has the important advantage that the clustering result that can be obtained on the selected...
As modeling and visualization applications proliferate, there arises a need to reduce three dimensional unorganized data points in reverse engineering. To meet the demand for both geometric and engineering fidelity of the reduction, a fuzzy-clustering-based reduction method is presented. As an effective extension to the existing pure geometric reduction methods, a hybrid heuristic is introduced. It...
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