Abstract
Genomic clustering is a big data application that uses the K-means (KM) clustering approach to discover hidden patterns and trends in genes for disease diagnosis, biological analysis, and tissue detection. The KM algorithm is highly dependent on the initial centroid because it determines the effectiveness, efficiency, computing resources, and local optima of the KM clustering. The existing initial centroid initialization approach traps local optima due to randomization and achieves high computational cost due to the enormous interrelated dimension. Therefore, the KM algorithm produces the lowest quality cluster and maximizes the computation time and resource consumption. To address this issue, this study has presented the Min–Max Kurtosis Mean Distance (MKMD) algorithm for big data clustering in a single machine environment. The MKMD algorithm enhances the effectiveness and efficiency of the KM algorithm by measuring the distance between data points of the minimum–maximum kurtosis dimension and their mean. The performance of the presented algorithm has been compared against KM, KM + + , ADV, MKM, Mean-KM, NFD, K-MAM, NRKM2, FMNN and MuKM algorithms using internal and external effectiveness evaluation criteria with efficiency assessment on sixteen genomic datasets. The experimental results reveal that the MKMDKM algorithm minimizes iterations, distance computation, data comparison, local optima, resource consumption, and improves cluster performance, effectiveness and efficiency with stable convergence and results as compared to other algorithms. According to the statistical analysis, the proposed MKMDKM algorithm has achieved statistical significance by employing the Friedman test and the post hoc test.
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Rehman MH, Liew CS, Abbas A et al (2016) Big data reduction methods: a survey. Data Sci Eng 1:265–284. https://doi.org/10.1007/s41019-016-0022-0
Mahmud MS, Huang JZ, Salloum S et al (2020) A survey of data partitioning and sampling methods to support big data analysis. Big Data Min Anal 3:85–101. https://doi.org/10.26599/BDMA.2019.9020015
Kacfah Emani C, Cullot N, Nicolle C (2015) Understandable big data: a survey. Comput Sci Rev 17:70–81. https://doi.org/10.1016/j.cosrev.2015.05.002
Pandey KK, Shukla D (2019) Challenges of big data to big data mining with their processing framework. In: 2018 8th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, pp 89–94
Reinartz T (1998) Similarity driven sampling for data mining. In: European Symposium on Principles of Data Mining and Knowledge Discovery. Springer, pp 423–431
Abualigah L, Diabat A, Mirjalili S et al (2021) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609. https://doi.org/10.1016/j.cma.2020.113609
Abualigah L, Yousri D, Abd Elaziz M et al (2021) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250. https://doi.org/10.1016/j.cie.2021.107250
Abualigah L, Diabat A, Elaziz MA (2021) Improved slime mould algorithm by opposition-based learning and levy flight distribution for global optimization and advances in real-world engineering problems. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-03372-w
Torrente A, Romo J (2021) Initializing k-means clustering by bootstrap and data depth. J Classif 38:232–256. https://doi.org/10.1007/s00357-020-09372-3
Liu T, Zhu J, Zhou J et al (2019) Initialization similarity clustering algorithm. Multimed Tools App. 78:33279–33296. https://doi.org/10.1007/s11042-019-7663-8
Kumar KM, Reddy ARM (2017) An efficient k-means clustering filtering algorithm using density based initial cluster centers. Inf Sci (Ny) 418–419:286–301. https://doi.org/10.1016/j.ins.2017.07.036
Ismkhan H (2018) I-k-means−+: an iterative clustering algorithm based on an enhanced version of the k-means. Pattern Recognit 79:402–413. https://doi.org/10.1016/j.patcog.2018.02.015
Zahra S, Ghazanfar MA, Khalid A et al (2015) Novel centroid selection approaches for k-means-clustering based recommender systems. Inf Sci (Ny) 320:156–189. https://doi.org/10.1016/j.ins.2015.03.062
Kushwaha N, Pant M, Kant S, Jain VK (2018) Magnetic optimization algorithm for data clustering. Pattern Recognit Lett 115:59–65. https://doi.org/10.1016/j.patrec.2017.10.031
Saxena A, Prasad M, Gupta A et al (2017) A review of clustering techniques and developments. Neurocomputing 267:664–681. https://doi.org/10.1016/j.neucom.2017.06.053
Jain AK (2010) Data clustering: 50 years beyond k-means. Pattern Recognit Lett 31:651–666. https://doi.org/10.1016/j.patrec.2009.09.011
Wang Z (2019) Mining data and metadata from the gene expression omnibus national center for biotechnology information. Biophys Rev 11:103–110
Xiao J, Yan Y, Zhang J, Tang Y (2010) A quantum-inspired genetic algorithm for k-means clustering. Expert Syst Appl 37:4966–4973. https://doi.org/10.1016/j.eswa.2009.12.017
Xu J, Xu B, Zhang W et al (2009) Stable initialization scheme for k-means clustering. Wuhan Univ J Nat Sci 14:24–28. https://doi.org/10.1007/s11859-009-0106-z
Kwedlo W, Iwanowicz P (2010) Using genetic algorithm for selection of initial cluster centers for the K-means method. In: Rutkowski L (ed) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2nd edn. Springer, Verlag Berlin Heidelberg, pp 165–172
Khondoker MR (2018) Big data clustering. Wiley StatsRef: Statistics Reference Online. John Wiley & Sons Ltd, Chichester, pp 1–10
Chen M, Ludwig SA, Li K (2017) Clustering in big data. In: Li K-C, Jiang H, Zomaya AY (eds) Big data management and processing. Chapman and Hall/CRC, New York, pp 333–346
Dafir Z, Lamari Y, Slaoui SC (2021) A survey on parallel clustering algorithms for big data. Artif Intell Rev 54:2411–2443. https://doi.org/10.1007/s10462-020-09918-2
Alguliyev RM, Aliguliyev RM, Sukhostat LV (2020) Efficient algorithm for big data clustering on single machine. CAAI Trans Intell Technol 5:9–14. https://doi.org/10.1049/trit.2019.0048
Bakhthemmat A, Izadi M (2020) Decreasing the execution time of reducers by revising clustering based on the futuristic greedy approach. J Big Data 7:6. https://doi.org/10.1186/s40537-019-0279-z
HajKacem MA Ben, N’Cir C E Ben, Essoussi N (2019) Overview of scalable partitional methods for big data clustering. In: Nasraoui O, N’Cir C E Ben (eds) Clustering Methods for Big Data Analytics, Unsupervised and Semi-Supervised Learning. Springer Nature, Switzerland, pp 1–23
Karmakar B, Das S, Bhattacharya S et al (2019) Tight clustering for large datasets with an application to gene expression data. Sci Rep 9:3053. https://doi.org/10.1038/s41598-019-39459-w
Overview G (2019) Microarray bioinformatics. Springer, New York
Mabu AM, Prasad R, Yadav R (2020) Mining gene expression data using data mining techniques: a critical review. J Inf Optim Sci 41:723–742. https://doi.org/10.1080/02522667.2018.1555311
Jiang D, Tang C, Zhang A (2004) Cluster analysis for gene expression data: a survey. IEEE Trans Knowl Data Eng 16:1370–1386. https://doi.org/10.1109/TKDE.2004.68
Hasan MS, Duan Z-H (2015) Hierarchical k-Means : A hybrid clustering algorithm and its application to study gene expression in lung adenocarcinoma. In: Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology. Elsevier, pp 51–67
Dong R, He L, He RL, Yau SS-T (2019) A novel approach to clustering genome sequences using internucleotide covariance. Front Genet. https://doi.org/10.3389/fgene.2019.00234
Fränti P, Sieranoja S (2019) How much can k-means be improved by using better initialization and repeats? Pattern Recognit 93:95–112. https://doi.org/10.1016/j.patcog.2019.04.014
Mousavian Anaraki SA, Haeri A, Moslehi F (2021) A hybrid reciprocal model of PCA and k-means with an innovative approach of considering sub-datasets for the improvement of k-means initialization and step-by-step labeling to create clusters with high interpretability. Pattern Anal Appl. https://doi.org/10.1007/s10044-021-00977-x
Arthur D, Vassilvitskii S (2007) K-means++: The advantages of careful seeding. In: SODA ’07: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. ACM Digital Library, pp 1027–1035
Fränti P, Sieranoja S (2018) K-means properties on six clustering benchmark datasets. Appl Intell 48:4743–4759. https://doi.org/10.1007/s10489-018-1238-7
Duwairi R, Abu-Rahmeh M (2015) A novel approach for initializing the spherical k-means clustering algorithm. Simul Model Pract Theory 54:49–63. https://doi.org/10.1016/j.simpat.2015.03.007
Peña J, Lozano J, Larrañaga P (1999) An empirical comparison of four initialization methods for the k-means algorithm. Pattern Recognit Lett 20:1027–1040. https://doi.org/10.1016/S0167-8655(99)00069-0
Birgin EG, Martinez JM, Ronconi DP (2003) Minimization subproblems and heuristics for an applied clustering problem. Eur J Oper Res 146:19–34. https://doi.org/10.1016/S0377-2217(02)00208-4
He J, Lan M, Tan CL, et al (2004) Initialization of cluster refinement algorithms: A review and comparative study. In: IEEE International Conference on Neural Networks - Conference Proceedings. IEEE Xplore, pp 297–302
Steinley D, Brusco MJ (2007) Initializing k-means batch clustering: a critical evaluation of several techniques. J Classif 24:99–121. https://doi.org/10.1007/s00357-007-0003-0
Celebi ME, Kingravi HA (2012) Deterministic initialization of the k-means algorithm using hierarchical clustering. Int J Pattern Recognit Artif Intell. https://doi.org/10.1142/S0218001412500188
Celebi ME, Kingravi HA, Vela PA (2013) A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst Appl 40:200–210. https://doi.org/10.1016/j.eswa.2012.07.021
Celebi ME, Kingravi HA (2015) Linear, deterministic and order-invariant initialization methods for the k-means clustering algorithm. In: Celebi ME (ed) Partitional Clustering Algorithms. Springer International Publishing, Cham, pp 79–98
Pourahmad S, Basirat A, Rahimi A, Doostfatemeh M (2020) Does determination of initial cluster centroids improve the performance of k-means clustering algorithm? comparison of three hybrid methods by genetic algorithm, minimum spanning tree, and hierarchical clustering in an applied study. Comput Math Methods Med. https://doi.org/10.1155/2020/7636857
Ji J, Pang W, Zheng Y et al (2015) An initialization method for clustering mixed mumeric and categorical data based on the density and distance. Int J Pattern Recognit Artif Intell. https://doi.org/10.1142/S021800141550024X
Erisoglu M, Calis N, Sakallioglu S (2011) A new algorithm for initial cluster centers in k-means algorithm. Pattern Recognit Lett 32:1701–1705. https://doi.org/10.1016/j.patrec.2011.07.011
Reddy D, Jana PK (2012) Initialization for k-means clustering using voronoi diagram. Procedia Technol 4:395–400. https://doi.org/10.1016/j.protcy.2012.05.061
Goyal M, Kumar S (2014) Improving the initial centroids of k-means clustering algorithm to generalize its applicability. J Inst Eng Ser B 95:345–350. https://doi.org/10.1007/s40031-014-0106-z
Poomagal S, Saranya P, Karthik S (2016) A novel method for selecting initial centroids in k-means clustering algorithm. Int J Intell Syst Technol Appl 15:230. https://doi.org/10.1504/IJISTA.2016.078347
Dhanabal S, Chandramathi S (2017) Enhancing clustering accuracy by finding initial centroid using k-minimum-average-maximum method. Int J Inf Commun Technol. https://doi.org/10.1504/IJICT.2017.10007027
Kazemi A, Khodabandehlouie G (2018) A new initialisation method for k-means algorithm in the clustering problem: data analysis. Int J Data Anal Tech Strateg 10:291. https://doi.org/10.1504/IJDATS.2018.094127
Li Y, Cai J, Yang H et al (2019) A novel algorithm for initial cluster center selection. IEEE Access 7:74683–74693. https://doi.org/10.1109/ACCESS.2019.2921320
Wang S, Liu X, Xiang L (2021) An improved initialisation method for K-means algorithm optimised by tissue-like P system. Int J Parallel, Emergent Distrib Syst 36:3–10. https://doi.org/10.1080/17445760.2019.1682144
Motwani M, Arora N, Gupta A (2019) A study on initial centroids selection for partitional clustering algorithms. In: Advances in Intelligent Systems and Computing. Springer Singapore, pp 211–220
Lakshmi MA, Victor Daniel G, Srinivasa Rao D (2019) Initial centroids for k-means using nearest neighbors and feature means. In: Advances in Intelligent Systems and Computing. Springer Singapore, pp 27–34
Chowdhury K, Chaudhuri D, Pal AK (2020) An entropy-based initialization method of k-means clustering on the optimal number of clusters. Neural Comput Appl 33:6965–6982. https://doi.org/10.1007/s00521-020-05471-9
Murugesan VP, Murugesan P (2020) A new initialization and performance measure for the rough k-means clustering. Soft Comput 24:11605–11619. https://doi.org/10.1007/s00500-019-04625-9
Fahad A, Alshatri N, Tari Z et al (2014) A survey of clustering algorithms for big data: taxonomy and empirical analysis. IEEE Trans Emerg Top Comput 2:267–279. https://doi.org/10.1109/TETC.2014.2330519
Pandove D, Goel S (2015) A comprehensive study on clustering approaches for big data mining. In: 2015 2nd International Conference on Electronics and Communication Systems (ICECS). IEEE, pp 1333–1338
Pandey KK, Shukla D (2019) A study of clustering taxonomy for big data mining with optimized clustering mapreduce model. Int J Emerg Technol 10:226–234
Pandove D, Goel S, Rani R (2018) Systematic review of clustering high-dimensional and large datasets. ACM Trans Knowl Discov Data 12:1–68. https://doi.org/10.1145/3132088
Abualigah L (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer Nature, Switzerland
Xiao Y, Yu J (2012) Partitive clustering (k -means family). Wiley Interdiscip Rev Data Min Knowl Discov 2:209–225. https://doi.org/10.1002/widm.1049
Kalyanakrishnan S (2017) K -means clustering. IIT,Bombay
Äyrämö S (2006) Knowledge mining using robust clustering. Jyväskylä University Printing House
Celikoglu A, Tirnakli U (2018) Skewness and kurtosis analysis for non-gaussian distributions. Phys A Stat Mech its Appl 499:325–334. https://doi.org/10.1016/j.physa.2018.02.035
Gentile C (2013) Using the kurtosis measure to identify clusters in wireless channel impulse responses. IEEE Trans Antennas Propag 61:3392–3395. https://doi.org/10.1109/TAP.2013.2253299
Judez L, Chaya C, De Miguel JM, Bru R (2006) Stratification and sample size of data sources for agricultural mathematical programming models. Math Comp Modell 43(5–6):530–535
Keskintürk T, Er Ş (2007) A genetic algorithm approach to determine stratum boundaries and sample sizes of each stratum in stratified sampling. Comp. Stat. Data Analysis 52(1):53–67
Étoré P, Jourdain B (2010) Adaptive optimal allocation in stratified sampling methods. Methodol Comput Appl Probab 12:335–360. https://doi.org/10.1007/s11009-008-9108-0
Rice JA (2007) Mathematical statistics and metastatistical analysis, Third Edit. Thomson Higher Education
Singh S (2003) Advanced sampling theory with applications, vol 1. Springer Netherlands, Dordrecht
Hoshida Y, Brunet J-P, Tamayo P et al (2007) Subclass mapping: identifying common subtypes in independent disease data sets. PLoS ONE. https://doi.org/10.1371/journal.pone.0001195
Hoshida Y (2010) Nearest template prediction: a single-sample-based flexible class prediction with confidence assessment. PLoS ONE. https://doi.org/10.1371/journal.pone.0015543
Dua, D. and Graff C (2019) UCI machine learning repository [http://archive.ics.uci.edu/ml]. Irvine, CA Univ California, Sch Inf Comput Sci
Feltes BC, Chandelier EB, Grisci BI, Dorn M (2019) CuMiDa: an extensively curated microarray database for benchmarking and testing of machine learning approaches in cancer research. J Comput Biol 26:376–386. https://doi.org/10.1089/cmb.2018.0238
De Souto MCP, Coelho ALV, Faceli K, et al (2012) A comparison of external clustering evaluation indices in the context of imbalanced data sets. In: 2012 Brazilian Symposium on Neural Networks. IEEE, pp 49–54
Rosenberg A, Hirschberg J (2007) V-Measure: a conditional entropy-based external cluster evaluation measure. In: Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning,. Association for Computational Linguistics, pp 410–420
Aggarwal CC, Reddy CK (2014) Data custering algorithms and applications. CRC Press, Boca Raton, United States
Gan G, Ma C, Wu J (2007) Data clustering theory, algorithms and applications. Society for Industrial and Applied Mathematics and American Statistical Association, Philadelphia, Pennsylvania
Yeh W-C, Lai C-M (2015) Accelerated simplified swarm optimization with exploitation search scheme for data clustering. PLoS ONE 10:e0137246. https://doi.org/10.1371/journal.pone.0137246
Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30
Deeb H, Sarangi A, Mishra D, Sarangi SK (2020) Improved black hole optimization algorithm for data clustering. J King Saud Univ-Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2020.12.013
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Pandey, K.K., Shukla, D. Min–max kurtosis mean distance based k-means initial centroid initialization method for big genomic data clustering. Evol. Intel. 16, 1055–1076 (2023). https://doi.org/10.1007/s12065-022-00720-3
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DOI: https://doi.org/10.1007/s12065-022-00720-3