Methodologies and Application

First Online: 22 March 2014

Cite this short article as: Tsai, C. Huang, K. Yang, C. et al. Soft Comput (2015) 19: 321. doi:10.1007/s00500-014-1255-3

• 4 Citations
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Abstract

This paper presents a higher-performance approach to lessen the time complexity of particle swarm optimization (PSO) and it is variants in solving the partitional clustering problem. The suggested method functions by adding two additional operators towards the PSO-based algorithms. The pattern reduction operator is aimed to lessen the computation time, by compressing each and every iteration patterns which are unlikely to alter the clusters that they belong after that as the multistart operator is aimed to enhance the caliber of the clustering result, by enforcing the variety of people to avoid the suggested method from getting stuck in local optima. To judge the performance from the suggested method, we compare it with several condition-of-the-art PSO-based methods in solving data clustering, image clustering, and codebook generation problems. Our simulation results indicate that although the suggested method considerably lessen the computation duration of PSO-based algorithms, but it may also give a clustering result that suits or outperforms the end result PSO-based algorithms on their own can offer.

Keywords

Clustering Particle swarm optimization Pattern reduction

Conveyed by W. Pedrycz.

References

Abraham A, Das S, Konar A (2007) Kernel based automatic clustering using modified particle swarm optimization formula, In: Proceedings from the Annual Conference on Genetic and Transformative Computation, pp 2–9

Ahmadi A, Karray F, Kamel M (2007a) Cooperative swarms for clustering phoneme data, In: Proceedings from the IEEE/SP Workshop on Record, Signal Processing, pp 606–610

Ahmadi A, Karray F, Kamel M (2007b) Multiple cooperating swarms for data clustering, In: Proceedings from the IEEE Swarm Intelligence Symposium, pp 206–212

Ahmadyfard A, Modares H (2008) Mixing PSO and (k) -way to enhance data clustering, In: Proceedings from the Worldwide Symposium on Telecommunications, pp 688–691

Bagirov AM, Ugon J, Webb D (2011) Fast modified global (k) -means formula for incremental cluster construction. Patt Recogn 44(4):866–876 CrossRef MATH

Banks A, Vincent J, Anyakoha C (2008) Overview of particle swarm optimization. part II: hybridisation, combinatorial, multicriteria and restricted optimization, and indicative applications. Nat Comput 7(1):109–124 CrossRef MATH MathSciNet

Bradley PS, Fayyad UM (1998) Refining initial points for (k) -means clustering, In: Proceedings from the Worldwide Conference on Machine Learning, pp 91–99

Bradley PS, Fayyad UM, Reina C (1998) Scaling clustering algorithms to large databases, In: Proceedings from the Worldwide Conference on Understanding Discovery and knowledge Mining, pp 9–15

Bratton D, Kennedy J (2007) Defining a typical for particle swarm optimization, In: Proceedings from the IEEE Swarm Intelligence Symposium, pp 120–127

Buzo A, Grey AH Junior, Grey RM, Markel JD (1980) Speech coding based on vector quantization. IEEE Trans Acoust Speech Signal Proc 28(5):562–574 CrossRef MATH MathSciNet

Cai W, Chen S, Zhang D (2007) Fast and powerful fuzzy (c) -means clustering algorithms incorporating local information for image segmentation. Patt Recogn 40(3):825–838 CrossRef MATH

Chen CY, Ye F (2004) Particle swarm optimization formula and it is application to clustering analysis, In: Proceedings from the IEEE Worldwide Conference on Networking, Sensing Control, 2:789– 794

Chen CY, Feng HM, Ye F (2006) Automatic particle swarm optimization clustering formula. Intern J Electr Eng 13(4):379–387

Cheng TW, Goldgof DB, Hall LO (1998) Fast fuzzy clustering. Fuzzy sets and systems 93(1):49–56

Chen Q, Yang J, Gou J (2005) Image compression method using improved PSO vector quantization, In: Proceedings from the Advances in Natural Computation, pp 490–495

Chiang MC, Tsai CW, Yang CS (2011) A period-efficient pattern reduction formula for (k) -means clustering. Info Sci 181(4):716–731 CrossRef

Cohen SCM, de Castro LN (2006) Data clustering with particle swarms, In: Proceedings from the IEEE Congress on Transformative Computation, pp 1792–1798

Das S, Abraham A, Konar A (2008) Automatic kernel clustering having a multi-elitist particle swarm optimization formula. Patt Recogn Lett 29(5):688–699 CrossRef

Derrac J, Garca S, Molina D, Herrera F (2011) An operating tutorial on using nonparametric record tests like a methodology for evaluating transformative and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18 CrossRef

Ding C, He X (2004) (K) -means clustering via principal component analysis, In: Proceedings from the Worldwide Conference on Machine Learning, 69:225–232

Elkan C (2003) While using triangular inequality to accelerate (k) -means, In: Proceedings from the Worldwide Conference on Machine Learning, pp 147–153

Engelbrecht AP (2006) Fundamentals of computational swarm intelligence. Wiley, West Sussex, England

Eschrich S, Ke J, Hall LO, Goldgof DB (2003) Fast accurate fuzzy clustering through data reduction. IEEE Trans Fuzzy Syst 11(2):262–270 CrossRef

Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based formula for finding clusters in large spatial databases with noise, In: Proceedings from the Worldwide Conference on Understanding Discovery and knowledge Mining, pp 226–231

Feng HM, Chen CY, Ye F (2007) Transformative fuzzy particle swarm optimization vector quantization learning plan in image compression. Exp Syst Appl 32(1):213–222 CrossRef

Getz G, Woman H, Kela I, Notterman DA, Domany E (2003) Coupled two-way clustering analysis of cancer of the breast and cancer of the colon gene expression data. Bioinformatics 19(9):1079–1089 CrossRef

Guha S, Meyerson A, Mishra N, Motwani R, O’Callaghan L (2003) Clustering data streams: theory and exercise. IEEE Trans Knowl Data Eng 15(3):515–528 CrossRef

Hammouda KM, Kamel MS (2004) Efficient phrase-based document indexing for web document clustering. IEEE Trans Knowl Data Eng 16(10):1279–1296 CrossRef

Jain AK, Murty MN, Flynn PJ (1999) Data clustering: an evaluation. ACM Comput Surv 31(3):264–323 CrossRef

Jarboui B, Cheikh M, Siarry P, Rebai A (2007) Combinatorial particle swarm optimization (CPSO) for partitional clustering problem. Appl Math Comput 192(2):337–345 CrossRef MATH MathSciNet

Karthi R, Arumugam S, RameshKumar K (2009) A manuscript discrete particle swarm clustering formula for data clustering, In: Proceedings from the Bangalore Annual Compute Conference, pp 16:1–16:4

Kaukoranta T, Frnti P, Nevalainen O (2000) A quick exact GLA according to code vector activity recognition. IEEE Trans Image Proc 9(8):1337–1342 CrossRef

Kekre HB, Sarode TK (2009) Fast codebook search formula for vector quantization using sorting technique, In: Proceedings from the Worldwide Conference on Advances in Computing, Communication and Control, pp 317–325

Kogan J (2007) Summary of clustering large and-dimensional data. Cambridge College Press, New You are able to MATH

Kulkarni RV, Venayagamoorthy GK (2011) Particle swarm optimization in wireless-sensor systems: a short survey. IEEE Trans Syst Man Cybernet Part C 41(2):262–267 CrossRef

Kuo RJ, Wang MJ, Huang TW (2011) A credit card applicatoin of particle swarm optimization formula to clustering analysis. Soft Comput 15(3):533–542 CrossRef

Lai JZC, Liaw YC, Liu J (2008) A quick VQ codebook generation formula using codeword displacement. Patt Recogn 41(1):315– 319

Lai JZC, Huang TJ, Liaw YC (2009) A quick (k) -means clustering formula using cluster center displacement. Patt Recogn 42(11):2551–2556 CrossRef MATH

Leuski A (2001) Evaluating document clustering for interactive information retrieval, In: Proceedings from the Worldwide Conference on Information and Understanding Management, pp 33–40

Li C, Zhou J, Kou P, Xiao J (2012) A manuscript chaotic particle swarm optimization based fuzzy clustering formula. Neurocomputing 83:98–109 CrossRef

Lughofer E (2008) Extensions of vector quantization for incremental clustering. Patt Recogn 41(3):995–1011 CrossRef MATH

Lu Y, Lu S, Fotouhi F, Deng Y, Brown SJ (2004) FGKA: a quick genetic (k) -means clustering formula, In: Proceedings from the ACM Symposium on Applied, Computing, pp 622–623

Marinakis Y, Marinaki M, Matsatsinis N (2008) A stochastic nature inspired metaheuristic for clustering analysis. Intern J Bus Apple Data Mining 3(1):30–44 CrossRef

Miranda V, Keko H, Duque AJ (2008) Stochastic star communication topology in transformative particle swarms (EPSO). Intern J Comput Apple Res 4(2):105–116 MathSciNet

Ng RT, Han J (2002) CLARANS: a technique for clustering objects for spatial data mining. IEEE Trans Knowl Data Eng 14(5):1003–1016 CrossRef

Niknam T, Amiri B, Olamaei J, Arefi A (2009) A competent hybrid transformative optimization formula according to PSO and SA for clustering. J Zhejiang Univ SCI A Ten(4):512–519 CrossRef MATH

Omran MGH, Salman AA, Engelbrecht AP (2002) Image classification using particle swarm optimization, In: Proceedings from the Asia-Off-shore Conference on Simulated Evolution and Learning, pp 370–374

Omran MGH, Engelbrecht AP, Salman AA (2005a) Particle swarm optimization way of image clustering. Intern J Patt Recogn Artif Apple 19(3):297–321

Omran MGH, Engelbrecht AP, Salman AA (2005b) Dynamic clustering using particle swarm optimization with application in without supervision image segmentation. Proc World Acad Sci Eng Technol 2005:199–204

Omran MGH, Salman AA, Engelbrecht AP (2006) Dynamic clustering using particle swarm optimization with application in image segmentation. Patt Rectal Appl 8(4):332–344 CrossRef MathSciNet

Ordonez C, Omiecinski E (2004) Efficient disk-based (k) -means clustering for relational databases. IEEE Trans Knowl Data Eng 16(8):909–921

Parsopoulos KE, Vrahatis MN (2010) Particle swarm optimization and intelligence: advances and applications. IGI Global Snippet

Paterlini S, Krink T (2006) Differential evolution and particle swarm optimisation in partitional clustering. Comput Stat Data Rectal 50(5):1220–1247 CrossRef MathSciNet

Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer as time passes-different acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255 CrossRef

Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization, In: Proceedings from the Congress on Transformative Computation, 3:1945–1950

Theodoridis S, Koutroumbas K (2009) Chapter 16: cluster validity, in pattern recognition, fourth edn. Academic Press, Boston

Tillett JC, Rao RM, Sahin F, Rao TM (2003) Particle swarm optimization for that clustering of wireless sensors, In: Proceedings of SPIE 5100:73–83

Tsai CW, Yang CS, Chiang MC (2007) A period efficient pattern reduction formula for (k) -means based clustering, In: Proceeding from the IEEE Worldwide Conference on Systems, Man and Cybernetics, pp 504–509

Tsai CW, Lin CF, Chiang MC, Yang CS (2010) A quick particle swarm optimization formula for vector quantization. ICIC Expr Lett Medicare Part B 1(2):137–143

van der Merwe DW, Engelbrecht AP (2003) Data clustering using particle swarm optimization, In: Proceedings of IEEE Congress on Transformative Computation, 1:215–220

Xiang S, Nie F, Zhang C (2008) Learning a Mahalanobis distance metric for data clustering and classification. Patt Recogn 41(12):3600–3612 CrossRef MATH

Xiao X, Dow jones ER, Eberhart R, Miled ZB, Oppelt RJ (2003) Gene clustering using self-organizing maps and particle swarm optimization, In: Proceedings from the Worldwide Symposium on Parallel and Distributed Processing

Xu R, Wunsch-II Electricity (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678 CrossRef

Xu R, Wunsch-II Electricity (2008) Clustering. Wiley, Hoboken, Nj

Xu W, Liu X, Gong Y (2003) Document clustering according to non-negative matrix factorization, In: Proceedings from the Worldwide ACM SIGIR Conference on Development and research in, Information Retrieval, pp 267–273

Yang CS, Chuang LY, Ke CH, Yang CH (2008) Comparative particle swarm optimization (CPSO) for solving optimization problems, In: Proceedings from the Worldwide Conference on Research, Innovation and Vision for future years in Computing Communication Technologies, pp 86–90

Zhang WF, Liu CC, Yan H (2010) Clustering of temporal gene expression data by regularized spline regression as well as an energy based similarity measure. Patt Recogn 43(12):3969–3976

Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: a competent data clustering way of large databases, In: Proceedings from the ACM SIGMOD Worldwide Conference on Control over Data, pp 103–114

information

Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

• Chun-Wei Tsai
  • 1
  • 2
• Ko-Wei Huang
  • 2
• Chu-Sing Yang
  • 2
• Ming-Chao Chiang
  • 3

  Email author

1. 1. Department of Applied Informatics and Multimedia Chia Nan College of Pharmacy and Science Tainan Taiwan, R.O.C.
2. 2. Institute laptop or computer and Communication Engineering, Department of Electrical Engineering National Cheng Kung College Tainan Taiwan, R.O.C.
3. 3. Department of Information Technology and Engineering National Sun Yat-sen College Kaohsiung Taiwan, R.O.C.

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