References |
: |
[1]Dubey AK, Gupta U, Jain S. Analysis of k-means clustering approach on the breast cancer Wisconsin dataset. International Journal of Computer Assisted Radiology and Surgery. 2016; 11(11):2033-47.
|
[Crossref] |
[Google Scholar] |
[2]Dubey AK, Gupta U, Jain S. Comparative study of K-means and fuzzy C-means algorithms on the breast cancer data. International Journal on Advanced Science, Engineering and Information Technology. 2018; 8(1):18-29.
|
[Crossref] |
[Google Scholar] |
[3]Mahmud MS, Rahman MM, Akhtar MN. Improvement of K-means clustering algorithm with better initial centroids based on weighted average. In international conference on electrical and computer engineering 2012 (pp. 647-50). IEEE.
|
[Crossref] |
[Google Scholar] |
[4]Margaret H. Data mining-“introductory and advanced concepts”.2006.
|
[Google Scholar] |
[5]Khandelwal A, Jain YK. An efficient k-means algorithm for the cluster head selection based on SAW and WPM. International Journal of Advanced Computer Research. 2018; 8(37):191-202.
|
[Crossref] |
[Google Scholar] |
[6]Pei J, Han J, Lu H, Nishio S, Tang S, Yang D. H-mine: hyper-structure mining of frequent patterns in large databases. In proceedings international conference on data mining 2001 (pp. 441-8). IEEE.
|
[Crossref] |
[Google Scholar] |
[7]Dubey AK, Dubey AK, Agarwal V, Khandagre Y. Knowledge discovery with a subset-superset approach for mining heterogeneous data with dynamic support. In CSI sixth international conference on software engineering 2012 (pp. 1-6). IEEE.
|
[Crossref] |
[Google Scholar] |
[8]Babu DB, Prasad RS, Umamaheswararao Y. Efficient frequent pattern tree construction. International Journal of Advanced Computer Research. 2014; 4(14):331-6.
|
[Google Scholar] |
[9]Li K, Cui L. A kernel fuzzy clustering algorithm with generalized entropy based on weighted sample. International Journal of Advanced Computer Research. 2014; 4(15):596-600.
|
[Google Scholar] |
[10]Horeis T, Sick B. Collaborative knowledge discovery & data mining: From knowledge to experience. In symposium on computational intelligence and data mining 2007 (pp. 421-8). IEEE.
|
[Crossref] |
[Google Scholar] |
[11]Zhou Z, Wu Z, Feng Y. Enhancing reliability throughout knowledge discovery process. In sixth international conference on data mining-workshops 2006 (pp. 754-8). IEEE.
|
[Crossref] |
[Google Scholar] |
[12]Mansour AM. Decision tree-based expert system for adverse drug reaction detection using fuzzy logic and genetic algorithm. International Journal of Advanced Computer Research. 2018; 8(36):110-28.
|
[Google Scholar] |
[13]Jamil A, Salam A, Amin F. Performance evaluation of top-k sequential mining methods on synthetic and real datasets. International Journal of Advanced Computer Research. 2017; 7(32):176-84.
|
[Crossref] |
[Google Scholar] |
[14]Lan GC, Hong TP, Tseng VS. An efficient projection-based indexing approach for mining high utility itemsets. Knowledge and Information Systems. 2014; 38(1):85-107.
|
[Crossref] |
[Google Scholar] |
[15]Singh B, Dubey V, Sheetlani J. A review and analysis on knowledge discovery and data mining techniques. International Journal of Advanced Technology and Engineering Exploration. 2018; 5(41):70-7.
|
[Crossref] |
[Google Scholar] |
[16]Dubey AK, Shandilya SK. Exploiting need of data mining services in mobile computing environments. In international conference on computational intelligence and communication networks 2010 (pp. 409-14). IEEE.
|
[Crossref] |
[Google Scholar] |
[17]Dubey AK, Gupta U, Jain S. Computational measure of cancer using data mining and optimization. In international conference on sustainable communication networks and application 2019 (pp. 626-32). Springer, Cham.
|
[Crossref] |
[Google Scholar] |
[18]Mahmuddin M, Yusof Y. Automatic estimation total number of cluster using a hybrid test-and-generate and K-means algorithm. In international conference on computer applications and industrial electronics 2010 (pp. 593-6). IEEE.
|
[Crossref] |
[Google Scholar] |
[19]Limungkura T, Vateekul P. Partition-based overlapping clustering using clusters parameters and relations. In international conference on knowledge and smart technology 2017 (pp. 144-9). IEEE.
|
[Crossref] |
[Google Scholar] |
[20]Aryuni M, Madyatmadja ED, Miranda E. Customer segmentation in XYZ bank using K-means and K-medoids clustering. In international conference on information management and technology 2018 (pp. 412-6). IEEE.
|
[Crossref] |
[Google Scholar] |
[21]Hafezi S, Moore AH, Naylor PA. Robust source counting and acoustic DOA estimation using density-based clustering. In 10th sensor array and multichannel signal processing workshop 2018 (pp. 395-9). IEEE.
|
[Crossref] |
[Google Scholar] |
[22]Malik H, Sangrasi DM, Dayo ZA. Comparative analysis of hybrid clustering algorithm on different dataset. In 8th international conference on electronics information and emergency communication 2018 (pp. 25-30). IEEE.
|
[Crossref] |
[Google Scholar] |
[23]Divya V, Devi KN. An efficient approach to determine number of clusters using principal component analysis. In international conference on current trends towards converging technologies 2018 (pp. 1-6). IEEE.
|
[Crossref] |
[Google Scholar] |
[24]Huan Z, Pengzhou Z, Zeyang G. K-means text dynamic clustering algorithm based on KL divergence. In IEEE/ACIS 17th international conference on computer and information science 2018 (pp. 659-63). IEEE.
|
[Crossref] |
[Google Scholar] |
[25]Singh R, Li K, Principe JC. Nearest-instance-centroid-estimation linear discriminant analysis (Nice Lda). In international conference on acoustics, speech and signal processing 2018 (pp. 2846-50). IEEE.
|
[Crossref] |
[Google Scholar] |
[26]Napoleon D, Lakshmi PG. An efficient K-means clustering algorithm for reducing time complexity using uniform distribution data points. In trendz in information sciences & computing 2010 (pp. 42-5). IEEE.
|
[Crossref] |
[Google Scholar] |
[27]Ganganath N, Cheng CT, Chi KT. Data clustering with cluster size constraints using a modified k-means algorithm. In international conference on cyber-enabled distributed computing and knowledge discovery 2014 (pp. 158-61). IEEE.
|
[Crossref] |
[Google Scholar] |
[28]Kapil S, Chawla M, Ansari MD. On K-means data clustering algorithm with genetic algorithm. In fourth international conference on parallel, distributed and grid computing 2016 (pp. 202-6). IEEE.
|
[Crossref] |
[Google Scholar] |
[29]Rajeswari K, Acharya O, Sharma M, Kopnar M, Karandikar K. Improvement in K-means clustering algorithm using data clustering. In international conference on computing communication control and automation 2015 (pp. 367-9). IEEE.
|
[Crossref] |
[Google Scholar] |
[30]Yuwono M, Su SW, Moulton BD, Nguyen HT. Data clustering using variants of rapid centroid estimation. IEEE Transactions on Evolutionary Computation. 2013; 18(3):366-77.
|
[Crossref] |
[Google Scholar] |
|