Correlation and congruence modulo based clustering technique and its application in energy classification
Tài liệu tham khảo
Khan, 2020, An enhanced multi density based clustering technique using density level partition (EDSCAN-DLP), J. Sci. Res., 120
Shaheen, 2021, CARM: context based association rule mining for conventional data, CMC-Comput. Mater. Cont., 68
Shaheen, 2011, Mining sustainability indicators to classify hydrocarbon development, Knowledge Based Syst., 24, 1159, 10.1016/j.knosys.2011.04.016
Muhammad, 2015, Supervised machine learning approaches: a survey, ICTACT J. Soft Comput., 5, 946, 10.21917/ijsc.2015.0133
Triguero, 2015, Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study, Knowl. Inf. Syst., 42, 245, 10.1007/s10115-013-0706-y
Dash, 2010, A hybridized K-means clustering approach for high dimensional dataset, Int. J. Eng. Sci. Technol., 2, 59, 10.4314/ijest.v2i2.59139
Celebi, 2013, A comparative study of efficient initialization methods for the k-means clustering algorithm, Expert Syst. Appl., 40, 200, 10.1016/j.eswa.2012.07.021
Franti, 2019, How much can k-means be improved by using better initialization and repeats?, Pattern Recognit., 93, 95, 10.1016/j.patcog.2019.04.014
Fahim, 2006, An efficient enhanced k-means clustering algorithm, J. Zhejiang Univ.-Sci. A, 7, 1626, 10.1631/jzus.2006.A1626
Xu, 2009, Stable initialization scheme for k-means clustering, Wuhan Univ. J. Nat. Sci., 14, 24, 10.1007/s11859-009-0106-z
Ye, 2006, Neighborhood density method for selecting initial cluster centers in K-means clustering, 189
Zhang, 2008, Improved K-means clustering algorithm, Vol. 5, 169
Cornuéjols, 2018, Collaborative clustering: why, when, what and how, Inf. Fusion, 39, 81, 10.1016/j.inffus.2017.04.008
Yera, 2017, Analysis of several decision fusion strategies for clustering validation. Strategy definition, experiments and validation, Pattern Recognit. Lett., 85, 42, 10.1016/j.patrec.2016.11.009
Yahyaoui, 2018, Unsupervised clustering of service performance behaviors, Inf. Sci., 422, 558, 10.1016/j.ins.2017.08.065
Dubiński, 2013, Sustainable development of mining mineral resources, J. Sustain. Min., 12, 1, 10.7424/jsm130102
Lior, 2010, Sustainable energy development: the present (2009) situation and possible paths to the future, Energy, 35, 3976, 10.1016/j.energy.2010.03.034
Shaheen, 2019, Application of labelled K-means clustering for GIS contract automation, J. Eng. Appl. Sci., 38, 15
Kantardzic, 2019
Muruganandham, 2018, Study on leaf segmentation using k-means and k-medoid clustering algorithm for identification of disease, Indian J. Public Health Res. Dev., 9, 289, 10.5958/0976-5506.2018.00456.4
Benesty, 2009, Pearson correlation coefficient, vol. 2
Canonical Correlation Analysis, 2007
Congruence, 2009
Kumar, 2020, Some new congruences modulo 5 for the general partition function, Russ. Math., 64, 73, 10.3103/S1066369X20070099
Rehman, 2019, A-RAFF: a ranked frequent pattern-growth subgraph pattern discovery approach, J. Internet Technol., 20, 257
Iqbal, 2016, Association rule mining using computational intelligence technique, Int. J. Comput. Sci. Inf. Sec., 14, 416
Rehman, 2010, An incremental density-based clustering technique for large datasets, 3
Aqeel, 2015, A supervised learning model for AGV perception in unstructured environment, 334
Capo, 2020, An efficient K-means clustering algorithm for tall data, Data Min. Knowl. Discov., 34, 776, 10.1007/s10618-020-00678-9
Arai, 2007, Hierarchical K-means: an algorithm for centroids initialization for K-means, Rep. Fac. Sci. Eng., 36, 25
Alrabea, 2013, Enhancing k-means algorithm with initial cluster centers derived from data partitioning along the data axis with PCA, J. Adv. Comput. Netw., 1, 137, 10.7763/JACN.2013.V1.28
Khan, 2004, Cluster center initialization algorithm for K-means clustering, Pattern Recognit. Lett., 25, 1293, 10.1016/j.patrec.2004.04.007
Karimov, 2015, Clustering quality improvement of K-Means using a hybrid evolutionary model, Procedia Comput. Sci., 38, 10.1016/j.procs.2015.09.143
Torrente, 2020, Initializing K-means clustering by Bootstrap and data depth, J. Classif.
Erisoglu, 2011, A new algorithm for initial cluster centers in k-means algorithm, Pattern Recognit. Lett., 32, 1701, 10.1016/j.patrec.2011.07.011
Syal, 2012, Innovative modified K-Mode clustering algorithm, Int. J. Eng. Res. Appl. (IJERA), 2, 390
Yedla, 2010, Enhancing K-means clustering algorithm with improved initial center, Int. J. Comput. Sci. Inf. Technol., 1, 121
Yuan, 2004, A new algorithm to get the initial centroids, Vol. 2, 1191
Deelers, 2007, Enhancing K-means algorithm with initial cluster centers derived from data partitioning along the data axis with the highest variance, Int. J. Comput. Sci., 2, 247
Fahim, 2009, An efficient k-means with good initial starting points, Georgian Electron. Sci. J.: Comput. Sci. Telecommun., 2, 47
Sun, 2002, An iterative initial-points refinement algorithm for categorical data clustering, Pattern Recognit. Lett., 23, 875, 10.1016/S0167-8655(01)00163-5