AGGLO-Hi clustering algorithm for gene expression micro array data using proximity measures

  • E. KavithaEmail author
  • R. Tamilarasan


Gene selection is imperative to clustering in light of gene articulation information, as a result of high Clustering quality. Clustering gene articulation information is a vital research subject in bioinformatics on the grounds that knowing which genes act correspondingly can prompt the disclosure of vital natural data. Many clustering systems have been proposed to the examination of gene articulation information got from microarray innovation. Clustering is one of the major procedures of investigating gene articulation information, fundamentally by contrasting gene articulation profiles or test articulation profiles. The Proposed strategy is an Agglo-Hi clustering algorithm which is accounted for the fuse of vicinity similarity estimates like Euclidean Distance, Manhattan Distance Chebyshev Distance, and Cosine Similarity for their execution. The technique is quality articulation information in microarray which is extricated and quality can be chosen from the preprocessed information, at that point the Agglo-Hi Clustering algorithm is utilized for quality information. The grouped information get approved utilizing legitimacy file and the outcome is gotten in light of nearness measures. To refine quality articulation information onto enhanced bunch quality by accelerating Unsupervised Learning stage and the execution of Agglo-Hi algorithm figures the Clustering quality, exactness and time unpredictability.


Unsupervised learning Clustering Microarray 



  1. 1.
    Bala Subramaniyan R, Hullermeier E, Weskamp N, Kamper J (2004) Clustering of gene expression data using a local shape-based similarity measure. Bioinformatics © Oxford University PressGoogle Scholar
  2. 2.
    BalaAnand M, Karthikeyan N, Karthik S (2018) Designing a framework for communal software: based on the assessment using relation modelling. Int J Parallel Prog.
  3. 3.
    Boeva V, Tsiporkova E (2010) A multi-purpose time series data standardization method, intelligent systems: from theory to practice. Springer-Verlag Berlin Heidelberg, SCI 299: 445–460Google Scholar
  4. 4.
    Borg A, Lavesson N, Boeva V (2013) Comparison of clustering approaches for gene expression data. In: Jaeger M et al. (Eds.) Twelfth Scandinavian Conference on Artificial Intelligence. IOS PressGoogle Scholar
  5. 5.
    Bryan J (2004) Problems in gene clustering based on gene expression data. J Multivar Anal 90:44–66MathSciNetCrossRefGoogle Scholar
  6. 6.
    Chalise P, Koestler DC, Bimali M, Yu Q, Fridley BL (2014) Integrative clustering methods for high-dimensional molecular data. Integrative Clustering Methods for High-Dimensional Molecular Data 3(3)Google Scholar
  7. 7.
    Chan EY, Ching WK, Ng MK, Huang JZ (2004) An optimization algorithm for clustering using weighted dissimilarity measures. Pattern Recogn 37(5):943–952CrossRefGoogle Scholar
  8. 8.
    Chen H, Zhang Y, Gutman I (2016) A kernel-based clustering method for gene selection with gene expression data. J Biomed Inform 62:12–20CrossRefGoogle Scholar
  9. 9.
    Costa IG, de A.T. de Carvalho F, de Souto MCP (2004) Comparative analysis of clustering methods for gene expression time course data. Genet Mol Biol 27(4):623–631CrossRefGoogle Scholar
  10. 10.
    Jiang D, Tang C, Zhang A (2004) Cluster analysis for gene expression data: a survey. IEEE Trans Knowl Data Eng 16(11):1370–1386CrossRefGoogle Scholar
  11. 11.
    Kerr G, Ruskin HJ, Crane M, Doolan P (2008) Techniques for clustering gene expression data. Comput Biol Med 38:283–293CrossRefGoogle Scholar
  12. 12.
    Kormaksson M, Booth JG, Figueroa ME et al (2012) Integrative model-based clustering of microarray methylation and expression data. Ann Appl Stat 6:1327–1347MathSciNetCrossRefGoogle Scholar
  13. 13.
    Liu J, Mohammed J, Carter J, Ranka S, Kahveci T, Baudis M (2006) Distance-based clustering of CGH data. Bioinformatics 22(16):1971–1978. CrossRefGoogle Scholar
  14. 14.
    Mahima KM, Govindaraj M (2015) An effective validation methodology of proximity measures for clustering gene expression microarray data. International Journal of Innovative Research in Computer and Communication Engineering 3(2)Google Scholar
  15. 15.
    Makolo A, Adigun T (2016) Optimization of clustering algorithms for gene expression data analysis using distance measures. Int J Comput Appl 139(13)Google Scholar
  16. 16.
    McNicholas PD, Murphy TB (2010) Model-based clustering of microarray expression data via latent Gaussian mixture models. Bioinformatics 26:2705–2712CrossRefGoogle Scholar
  17. 17.
    Moller-Levet C, Cho KH, Yin H, Wolkenhauer O (2003) Clustering of gene expression time-series data, Technical ReportGoogle Scholar
  18. 18.
    Pirim H, Ekşioğlu B, Perkins A, Yüceer C (2012) Clustering of high throughput gene expression data. Comput Oper Res 39(12):3046–3061. MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Romdhane LB, Shili H, Ayeb B (2009) Mining microarray gene expression data with unsupervised possibilistic clustering and proximity graphs. Springer Science Business Media, LLCGoogle Scholar
  20. 20.
    Sarmah S, Bhattacharyya DK (2010) An effective technique for clustering incremental gene expression data. International Journal of Computer Science Issues 7(3):3Google Scholar
  21. 21.
    Seal S, Komarina S, Aluru S (2005) An optimal hierarchical clustering algorithm for gene expression data. Inf Process Lett 93:143–147MathSciNetCrossRefGoogle Scholar
  22. 22.
    Visvanathan M, Adagarla BS, Gerald HL, Smith P (2009) Cluster validation: an integrative method for cluster analysis. IEEE International Conference on Bioinformatics and Biomedicine WorkshopGoogle Scholar
  23. 23.
    Yeung KY, Medvedovic M, Bumgarner RE (2003) Clustering gene-expression data with repeated measurements. Genome Biol 4(5):R34CrossRefGoogle Scholar
  24. 24.
    Zareizadeh Z, Helfroush MS, Rahideh A, Kazemi K (2018) A robust gene clustering algorithm based on clonal selection in multiobjective optimization framework. Expert Systems with ApplicationsGoogle Scholar
  25. 25.
    Zhang W, Zhao D, Wang X (2013) Agglomerative clustering via maximum incremental path integral. Pattern Recogn 46:3056–3065CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.University College of EngineeringVillupuramIndia
  2. 2.University College of EngineeringPattukotaiIndia

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