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48th ACM Annual Southeast Regional Conference (SE)

作者： Hung, Chih-Cheng Sun, Mojia Southern Polytech State Univ 1100 S Marietta Pkwy Marietta GA 30060 USA

ISBN: (纸本)9781450300643

In this paper the ant colony optimization (ACO) is used in the k-means algorithm for improving the image segmentation. The learning mechanism of this algorithm is formulated by using the ACO meta-heuristic. As the pheromone dominates the exploration of ants for problem solutions, preliminary experiments on pheromone's update are reported. Two methods for defining and updating pheromone values are proposed and tested: one with the spatial coordinate distances and the other without using such a distance. The ACO improves the k-means algorithm by making it less dependent on the initial parameters.

关键词： k-means clustering algorithm Ant colony optimization Swarm Intelligence

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Semi-supervised Rough Cost/Benefit Decisions

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FUNDAMENTA INFORMATICAE 2009年第2期94卷 233-244页

作者： Lingras, Pawan Chen, Min Miao, Duoqian St Marys Univ Dept Math & Comp Sci Halifax NS B3H 3C3 Canada Tongji Univ Sch Elect & Informat Engn Shanghai 201804 Peoples R China Jiao Tong Univ Minist Educ China Key Lab Embedded Syst & Serv Comp Shanghai 201804 Peoples R China

Most of the business decisions are based on cost and benefit considerations. Data mining techniques that make it possible for the businesses to incorporate financial considerations will be more meaningful to the decision makers. Decision theoretic framework has been helpful in providing a better understanding of classification models. This study describes a semi-supervised decision theoretic rough set model. The model is based on an extension of decision theoretic model proposed by Yao. The proposal is used to model financial cost/benefit scenarios for a promotional campaign in a real-world retail store.

关键词： Rough sets Rough approximation Probability Decision theory Cost/benefit analysis k-means clustering algorithm

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An initialization method for the k-means algorithm using neighborhood model

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2009年第3期58卷 474-483页

作者： Cao, Fuyuan Liang, Jiye Jiang, Guang Shanxi Univ Sch Comp & Informat Technol Taiyuan 030006 Shanxi Peoples R China Minist Educ Key Lab Computat Intelligence & Chinese Informat Taiyuan 030006 Peoples R China Chinese Acad Sci Key Lab Intelligent Informat Proc Inst Comp Technol Beijing 100190 Peoples R China

As a simple clustering method, the traditional k-means algorithm has been widely discussed and applied in pattern recognition and machine learning. However, the k-means algorithm could not guarantee unique clustering result because initial cluster centers are chosen randomly. In this paper, the cohesion degree of the neighborhood of an object and the coupling degree between neighborhoods of objects are defined based on the neighborhood-based rough set model. Furthermore, a new initialization method is proposed, and the corresponding time complexity is analyzed as well. We study the influence of the three norms on clustering, and compare the clustering results of the k-means with the three different initialization methods. The experimental results illustrate the effectiveness of the proposed method. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Neighborhood model Initial cluster centers Cohesion degree Coupling degree k-means clustering algorithm

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Application of an unsupervised artificial neural network technique to multivariant surface water quality data

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ECOLOGICAL RESEARCH 2009年第1期24卷 163-173页

作者： Cinar, Oezer Merdun, Hasan Kahramanmaras Sutcu Imam Univ Fac Engn & Architecture Dept Environm Engn TR-46060 Kahramanmaras Turkey Kahramanmaras Sutcu Imam Univ Fac Agr Dept Agr Engn TR-46060 Kahramanmaras Turkey

Surface water contamination from agricultural and urban runoff and wastewater discharges from industrial and municipal activities is of major concern to people worldwide. Classical models can be insufficient to visualise the results because the water quality variables used to describe dynamic pollution sources are complex, multivariable, and nonlinearly related. Artificial intelligence techniques with the ability to analyse multivariant water quality data by means of a sophisticated visualisation capacity can offer an alternative to current models. In this study, the kohonen self-organising feature maps (SOM) neural network was initially applied to analyse the complex nonlinear relationships among multivariable surface water quality variables using the component planes of the variables to determine the complex behaviour of water quality parameters. The dependencies between water quality variables were extracted and interpreted using the pattern analysis visualised in component planes. For further investigation, the k-means clustering algorithm was used to determine the optimal number of clusters by partitioning the maps and utilising the Davies-Bouldin clustering index, leading to seven groups or clusters corresponding to water quality variables. The results reveal that the concentrations of Na, k, Cl, NH4-N, NO2-N, o-PO4, component planes of organic matter (pV), and dissolved oxygen (DO) were significantly affected by seasonal changes, and that the SOM technique is an efficient tool with which to analyse and determine the complex behaviour of multidimensional surface water quality data. These results suggest that this technique could also be applied to other environmentally sensitive areas such as air and groundwater pollution.

关键词： kohonen self-organising feature maps clustering k-means clustering algorithm Surface water quality Variable dependencies

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作者： Veni, Rushikesh University of Nevada Las Vegas

学位级别：M.S.C.S.

Document clustering or unsupervised document classification is an automated process of grouping documents with similar content. A typical technique uses a similarity function to compare documents. In the literature, many similarity functions such as dot product or cosine measures are proposed for the comparison operator. For the thesis, we evaluate the effects a similarity function may have on clustering. We start by representing a document and a query, both as a vector of high-dimensional space corresponding to the keywords followed by using an appropriate distance measure in k-means to compute similarity between the document vector and the query vector to form clusters. Based on these clusters we decide the best distance metric for the document set used. Next, we compute time complexities for different similarity functions for the same model and document set based on the number of iterations and number of clusters.

关键词： Canberra distances Chi-Square Data mining Distances Document clustering Euclidean distances Execution time Information retrieval k-means clustering algorithm Similarity functions

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Improved k-means clustering algorithm for exploring local protein sequence motifs representing common structural property

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2005年第3期4卷 255-265页

作者： Zhong, W Altun, G Harrison, R Tai, PC Pan, Y Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA Georgia State Univ Dept Biol Atlanta GA 30303 USA

Information about local protein sequence motifs is very important to the analysis of biologically significant conserved regions of protein sequences. These conserved regions can potentially determine the diverse conformation and activities of proteins. In this work, recurring sequence motifs of proteins are explored with an improved k-means clustering algorithm on a new dataset. The structural similarity of these recurring sequence clusters to produce sequence motifs is studied in order to evaluate the relationship between sequence motifs and their structures. To the best of our knowledge, the dataset used by our research is the most updated dataset among similar studies for sequence motifs. A new greedy initialization method for the k-means algorithm is proposed to improve traditional k-means clustering techniques. The new initialization method tries to choose suitable initial points, which are well separated and have the potential to form high-quality clusters. Our experiments indicate that the improved k-means algorithm satisfactorily increases the percentage of sequence segments belonging to clusters with high structural similarity. Careful comparison of sequence motifs obtained by the improved and traditional algorithms also suggests that the improved k-means clustering algorithm may discover some relatively weak and subtle sequence motifs, which are undetectable by the traditional k-means algorithms. Many biochemical tests reported in the literature show that these sequence motifs are biologically meaningful. Experimental results also indicate that the improved k-means algorithm generates more detailed sequence motifs representing common structures than previous research. Furthermore, these motifs are universally conserved sequence patterns across protein families, overcoming some weak points of other popular sequence motifs. The satisfactory result of the experiment suggests that this new k-means algorithm may be applied to other areas of bioinformatics resea

关键词： k-means clustering algorithm protein structure sequence motif

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Modifying Genetic algorithm with Species and Sexual Selection by using k-means algorithm

Modifying Genetic Algorithm with Species and Sexual Selectio...

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IEEE International Advance Computing Conference

作者： Patel, Rahila Raghuwanshi, M. M. Jaiswal, Anil N. GHRCE CSE M Tech Sem 4 Nagpur Ms India GHRCE Nagpur Ms India NYSS Coll Engn & Res Nagpur Maharashtra India

ISBN: (纸本)9781424429271

Presently, the optimization concept plays an important role in the problems related to engineering management and commerce etc. Recent trends in optimization, points towards the genetic algorithm and evolutionary approaches. Different genetic algorithms are proposed, designed and implemented for the single objective as well as for the multiobjective problems. GAS3[2006](Genetic algorithm with Species and Sexual Selection) proposed by *** and *** is a distributed Quasi steady state real-coded genetic algorithm. In this work, we have modified GAS3 algorithm. We introduce a reclustering module in GAS3 after simple distance based parameter less clustering (species formation). GAS3kM (Modifying Genetic algorithm with Species and Sexual Selection by using k-means algorithm) uses k-means clustering algorithm for reclustering. Experimental results show that GAS3kM has outperformed GAS3 algorithm when tested on unimodal and multimodal test functions.

关键词： Genetic algorithm clustering algorithms optimization evolutionary algorithm k-means clustering algorithm Pattern recognition

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Acceleratedk-means clustering algorithm for colour image quantization

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The Journal of Photographic Science 2008年第1期56卷 29-40页

Thek-means clustering algorithm is a commonly used algorithm for palette design. If an adequate initial palette is selected, a good quality reconstructed image of a compressed colour image can be achieved. The major problem is that a great deal of computational cost is consumed. To accelerate thek-means clustering algorithm, two test conditions are employed in the proposed algorithm. From the experimental results, it is found that the proposed algorithm significantly cuts down the computational cost of thek-means clustering algorithm without incurring any extra distortion.

关键词： COLOUR IMAGE QUANTIZATION k-means clustering algorithm VECTOR QUANTIZATION LBG algorithm

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Precision of Rough Set clustering

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6th International Conference on Rough Sets and Current Trends in Computing

作者： Lingras, Pawan Chen, Min Miao, Duoqian St Marys Univ Dept Math & Comp Sci Halifax NS B3H 3C3 Canada Tongji Univ Sch Elect & Informat Engn Shanghai 201804 Peoples R China

ISBN: (纸本)9783540884231

Conventional clustering algorithms categorize an object into precisely one cluster. In many applications, the membership of some of the objects to a cluster can be ambiguous. Therefore, an ability to specify membership to multiple clusters can be useful in real world applications. Fuzzy clustering makes it possible to specify the degree to which a given object belongs to a cluster. In Rough set representations, an object may belong to more than one cluster, which is more flexible than the conventional crisp clusters and less verbose than the fuzzy clusters. The unsupervised nature of fuzzy and rough algorithms means that there is a choice about the level of precision depending on the choice of parameters. This paper describes how one can vary the precision of the rough set clustering and studies its effect on synthetic and real world data sets.

关键词： Rough sets k-means clustering algorithm precision

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International Symposium on Communication and Information Technologies

作者： Leauhatong, Thurdsak Hamamoto, kazuhiko Atsuta, kiyoaki kondo, Shozo Tokai Univ Grad Sch Sci & Technol Hiratsuka Kanagawa 25912 Japan Tokai Univ Sch Informat & Telecommun engn Hiratsuka Kanagawa 25912 Japan

ISBN: (纸本)9781424423354

This paper proposes a new similarity measure for the content-based image retrieval (CBIR) systems. The similarity measure is based on the multidimensional generalization of the Wald-Wolfowitz (MWW) runs test and the k-means clustering algorithm. The performance comparisons between the proposed method and the current CBIR method based on MWW runs test were performed, and it can be seen that the proposed methods outperform the current method in the sense that the proposed method provides higher performance than the current method for the same computational time.

关键词： Color Histogram Content-Based Image Retrieval k-means clustering algorithm minimal spanning tree Wald-Wolfowitz runs test

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