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2009 International Conference of Soft Computing and Pattern Recognition

4-7 Dec. 2009

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  • [Front cover]

    Publication Year: 2009, Page(s): C1
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  • [Title page i]

    Publication Year: 2009, Page(s): i
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  • [Title page iii]

    Publication Year: 2009, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2009, Page(s): iv
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  • Table of contents

    Publication Year: 2009, Page(s):v - xv
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  • Welcome from the General Chairs

    Publication Year: 2009, Page(s): xvi
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  • Welcome from the Program Chairs

    Publication Year: 2009, Page(s): xvii
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  • Conference Committees

    Publication Year: 2009, Page(s):xviii - xix
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  • Program Committee

    Publication Year: 2009, Page(s):xx - xxiv
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  • Additional reviewers

    Publication Year: 2009, Page(s): xxv
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  • Special Sessions Committees

    Publication Year: 2009, Page(s): xxvi
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  • Technical Support and Sponsors

    Publication Year: 2009, Page(s): xxvii
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  • Plenary Keynotes

    Publication Year: 2009, Page(s):xxviii - xxxiv
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (92 KB)

    Provides an abstract for each of the plenary presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings. View full abstract»

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  • Industrial Talk

    Publication Year: 2009, Page(s): xxxv
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (79 KB)

    Multi-level approaches become widely applicable to complex tasks of data analysis, wherein complexity may refer to what we try to learn from data, to technical details that prevent users from understanding data mining algorithms, or to data volumes that are too large to keep using standard algorithms. Levels (or layers) can be understood in various ways, as in artificial neural networks, ensembles... View full abstract»

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  • BiSim: A Simple and Efficient Biclustering Algorithm

    Publication Year: 2009, Page(s):1 - 6
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (443 KB) | HTML iconHTML

    Analysis of gene expression data includes classification of the data into groups and subgroups based on similar expression patterns. Standard clustering methods for the analysis of gene expression data only identifies the global models while missing the local expression patterns. In order to identify the missed patterns biclustering approach has been introduced. Various biclustering algorithms hav... View full abstract»

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  • CBGP: Classification Based on Gradual Patterns

    Publication Year: 2009, Page(s):7 - 12
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (461 KB) | HTML iconHTML

    In this paper, we address the issue of mining gradual classification rules. In general, gradual patterns refer to regularities such as "The older a person, the higher his salary''. Such patterns are extensively and successfully used in command-based systems, especially in fuzzy command applications. However, in such applications, gradual patterns are supposed to be known and/or provided by an expe... View full abstract»

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  • Initial Result of Clustering Strategy to Euclidean TSP

    Publication Year: 2009, Page(s):13 - 18
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (391 KB) | HTML iconHTML

    There has been growing interest in studying combinatorial optimization problems by clustering strategy, with a special emphasis on the traveling salesman problem (TSP). Since TSP naturally arises as a sub problem in many transportation, manufacturing and various logistics application, this problem has caught much attention of mathematicians and computer scientists. A clustering strategy will decom... View full abstract»

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  • A New Clustering Method Based on Weighted Kernel K-Means for Non-linear Data

    Publication Year: 2009, Page(s):19 - 24
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (681 KB) | HTML iconHTML

    Clustering is the process of gathering objects into groups based on their feature's similarity. In this paper, we concentrate on Weighted Kernel K-Means method for its capability to manage nonlinear separability and high dimensionality in the data. A new slight modification of WKM algorithm has been proposed and tested on real Rice data. The results show that the accuracy of proposed algorithm is ... View full abstract»

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  • A Review of Recent Alignment-Free Clustering Algorithms in Expressed Sequence Tag

    Publication Year: 2009, Page(s):25 - 30
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (383 KB) | HTML iconHTML

    Expressed sequence tags (ESTs) are short single pass sequence reads derived from cDNA libraries, they have been used for gene discovery, detection of splice variants, expression of genes and also transciptome analysis. Clustering of ESTs is a vital step before they can be processed further. Currently there are many EST clustering algorithms available. Basically they can be generalized into two bro... View full abstract»

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  • Parameter Study and Optimization of a Color-Based Object Classification System

    Publication Year: 2009, Page(s):31 - 36
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (331 KB) | HTML iconHTML

    Typical computer vision systems usually include a set of components such as a preprocessor, a feature extractor, and a classifier that together represent an image processing pipeline. For each component there are different operators available. Each operator has a different number of parameters with individual parameter domains. The challenge in developing a computer vision system is the optimal ch... View full abstract»

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  • Arrhythmia Beat Classification Using Pruned Fuzzy K-Nearest Neighbor Classifier

    Publication Year: 2009, Page(s):37 - 42
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (405 KB) | HTML iconHTML

    In this paper, pruned fuzzy k-nearest neighbor (PFKNN) classifier is proposed to classify different types of arrhythmia beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification which can be very time consum... View full abstract»

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  • League Championship Algorithm: A New Algorithm for Numerical Function Optimization

    Publication Year: 2009, Page(s):43 - 48
    Cited by:  Papers (10)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (375 KB) | HTML iconHTML

    Inspired by the competition of sport teams in a sport league, an algorithm is presented for optimizing nonlinear continuous functions. A number of individuals as sport teams compete in an artificial league for several weeks (iterations). Based on the league schedule in each week, teams play in pairs and the outcome is determined in terms of win or loss, given known the team's playing strength (fit... View full abstract»

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  • An Improved Discrete Particle Swarm Optimization in Evacuation Planning

    Publication Year: 2009, Page(s):49 - 53
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (368 KB) | HTML iconHTML

    In any flash flood evacuation operation, vehicle assignment at the inundated areas is vital to help eliminate loss of life. Vehicles of various types and capacities are used to evacuate victims to relief centers. This paper examines combinatorial optimization approach with the objective function to assign a specified number of vehicles with maximum number of evacuees to the potential inundated are... View full abstract»

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  • Cat Swarm Optimization for Clustering

    Publication Year: 2009, Page(s):54 - 59
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (360 KB) | HTML iconHTML

    Cat swarm optimization (CSO) is one of the new heuristic optimization algorithm which based on swarm intelligence. Previous research shows that this algorithm has better performance compared to the other heuristic optimization algorithms: Particle swarm optimization (PSO) and weighted-PSO in the cases of function minimization. In this research a new CSO algorithm for clustering problem is proposed... View full abstract»

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  • Implementing Particle Swarm Optimization to Solve Economic Load Dispatch Problem

    Publication Year: 2009, Page(s):60 - 65
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (404 KB) | HTML iconHTML

    Economic Load Dispatch (ELD) is one of an important optimization tasks which provides an economic condition for a power systems. In this paper, Particle Swarm Optimization (PSO) as an effective and reliable evolutionary based approach has been proposed to solve the constraint economic load dispatch problem. The proposed method is able to determine, the output power generation for all of the power ... View full abstract»

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