Abstract

Data mining is used for the knowledge discovery in the area of engineering, medical diagnosis, business analytics, etc. The main aim of this paper is to explore the technological findings in the several fields suggested above and analysis the methods on the basis of the capability of knowledge discovery. In this regard several methodologies have been discussed which are previously published for the analysis. This analysis provides us a proper insight regarding the gaps, advantages and future implications and directions.

Keyword

Data mining, Apriori, FP-Growth, SPADE, ECLAT.

Cite this article

Refference

[1][1]Agrawal R, Srikant R. Fast algorithms for mining association rules. In proceeding of international conference on very large data bases, VLDB 1994 (pp. 487-99).

[2][2]Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation. In ACM SIGMOD record 2000 (pp. 1-12). ACM.

[3][3]Zaki MJ, Parthasarathy S, Ogihara M, Li W. New algorithms for fast discovery of association rules. In KDD 1997 (pp. 283-6).

[4][4]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.

[5][5]Agarwal RC, Aggarwal CC, Prasad VV. A tree projection algorithm for generation of frequent item sets. Journal of Parallel and Distributed Computing. 2001; 61(3):350-71.

[6][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 IEEE international conference on data mining 2001 (pp. 441-8). IEEE.

[7][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.

[8][8]Babu DB, Prasad RS, Umamaheswararao Y. Efficient frequent pattern tree construction. International Journal of Advanced Computer Research. 2014; 4(14):331-6.

[9][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.

[10][10]Horeis T, Sick B. Collaborative knowledge discovery & data mining: from knowledge to experience. In IEEE symposium on computational intelligence and data mining 2007 (pp. 421-8). IEEE.

[11][11]Feng Y, Wu Z, Zhou Z. Enhancing reliability throughout knowledge discovery process. In international conference on data mining workshop 2006 (pp. 754-8). IEEE.

[12][12]Vityaev EE, Kovalerchuk BY. Relational methodology for data mining and knowledge discovery. Intelligent Data Analysis. 2008; 12(2):189-210.

[13][13]Fournier-Viger P, Wu CW, Zida S, Tseng VS. FHM: faster high-utility itemset mining using estimated utility co-occurrence pruning. In international symposium on methodologies for intelligent systems 2014 (pp. 83-92). Springer, Cham.

[14][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.

[15][15]Song W, Liu Y, Li J. BAHUI: fast and memory efficient mining of high utility item sets based on bitmap. International Journal of Data Warehousing and Mining. 2014; 10(1):1-15.

[16][16]Tseng VS, Shie BE, Wu CW, Philip SY. Efficient algorithms for mining high utility item sets from transactional databases. IEEE Transactions on Knowledge and Data Engineering. 2013; 25(8):1772-86.

[17][17]Rashidi P, Cook DJ. Mining sensor streams for discovering human activity patterns over time. In international conference on data mining 2010 (pp. 431-40). IEEE.

[18][18]Wang B, Chen D, Shi B, Zhang J, Duan Y, Chen J, et al. Comprehensive association rules mining of health examination data with an extended FP-growth method. Mobile Networks and Applications. 2017; 22(2):267-74.

[19][19]Xu F, Lu H. The application of FP-Growth algorithm based on distributed intelligence in wisdom medical treatment. International Journal of Pattern Recognition and Artificial Intelligence. 2017; 31(4):1-11.

[20][20]Pei B, Wang X, Wang F. Parallelization of FP-growth algorithm for mining probabilistic numerical data based on MapReduce. In international symposium on computational intelligence and design 2016 (pp. 223-6). IEEE.

[21][21]Makanju A, Farzanyar Z, An A, Cercone N, Hu ZZ, Hu Y. Deep parallelization of parallel FP-growth using parent-child Map Reduce. In international conference on Big Data 2016 (pp. 1422-31). IEEE.

[22][22]Shrivastava S, Johari PK. Analysis on high utility infrequent itemsets mining over transactional database. In international conference on recent trends in electronics, information & communication technology 2016 (pp. 897-902). IEEE.

[23][23]Vanahalli MK, Patil N. Association analysis of significant frequent colossal itemsets mined from high dimensional datasets. In international conference on electrical, computer and electronics engineering 2016 (pp. 258-63). IEEE.

[24][24]Li C, Dong X, Dong X, Ren X. FP-growth based method for mining infrequent and frequent itemsets with 2-level minimum support. In international conference on computer science and network technology 2016 (pp. 263-7). IEEE.

[25][25]Ghorbani M, Abessi M. A new methodology for mining frequent itemsets on temporal data. IEEE Transactions on Engineering Management. 2017; 64(4):566-73.

[26][26]He B, Pei J, Zhang H. The mining algorithm of frequent itemsets based on mapreduce and FP-tree. In international conference on computer network, electronic and automation 2017(pp. 108-11). IEEE.

[27][27]Phuong N, Duy ND. Constructing a new algorithm for high average utility itemsets mining. In international conference on system science and engineering 2017 (pp. 273-8). IEEE.

[28][28]Zulkurnain NF, Shah A. HYBRID: an efficient unifying process to mine frequent itemsets. In 3rd international conference on engineering technologies and social sciences 2017 (pp. 1-5). IEEE.

[29][29]Hong TP, Lin KY, Lin CW, Vo B. An incremental mining algorithm for erasable itemsets. In international conference on innovations in intelligent systems and applications 2017 (pp. 286-9). IEEE.

[30][30]Ismail W, Hassan MM, Fortino G. Productive-associated periodic high-utility itemsets mining. In international conference on networking, sensing and control 2017 (pp. 637-42). IEEE.

[31][31]Klangwisan K, Amphawan K. Mining weighted-frequent-regular itemsets from transactional database. In international conference on knowledge and smart technology 2017 (pp. 66-71). IEEE

[32][32]Jiang H, He X. An improved algorithm for frequent itemsets mining. In international conference on advanced cloud and big data 2017 (pp. 314-7). IEEE.

[33][33]Mohammed MA, Al-Khafaji H. Maximal itemsets mining algorithm based on bees algorithm. In annual conference on new trends in information & communications technology applications 2017 (pp. 1-6). IEEE.

[34][34]Subbulakshmi B, Dharini B, Deisy C. Recent weighted maximal frequent itemsets mining. In international conference on IoT in social, mobile, analytics and cloud 2017 (pp. 391-7). IEEE.

[35][35]Khode S, Mohod S. Mining high utility itemsets using TKO and TKU to find top-k high utility web access patterns. In international conference of electronics, communication and aerospace technology 2017 (pp. 504-9). IEEE.

[36][36]Wang H, Li F, Tang D, Wang Z. Research on data stream mining algorithm for frequent itemsets based on sliding window model. In international conference on big data analysis 2017 (pp. 259-63). IEEE.

[37][37]Bai A, Deshpande PS, Dhabu M. Selective database projections based approach for mining high-utility itemsets. IEEE Access. 2018; 6:14389-409.

[38][38]Nan J, Cheng L, Yi L. A similar safety systematics model for accident cases data mining support. Procedia Computer Science. 2018; 131:929-36.

[39][39]Rocha A, Camacho R, Ruwaard J, Riper H. Using multi-relational data mining to discriminate blended therapy efficiency on patients based on log data. Internet Interventions. 2018; 12:176-80.

[40][40]Lu W, Xiao R, Yang J, Li H, Zhang W. Data mining-aided materials discovery and optimization. Journal of Materiomics. 2017; 3(3):191-201.

[41][41]Rojas WC, Quispe FM, Villegas CM. Augmented visualization for data-mining models. Procedia Computer Science. 2015; 55:650-9.

[42][42]Vadim K. Overview of different approaches to solving problems of data mining. Procedia Computer Science. 2018; 123:234-9.

[43][43]Darrab S, Ergenç B. Vertical pattern mining algorithm for multiple support thresholds. Procedia Computer Science. 2017; 112 (2017):417–26.

[44][44]Jabbour S, El Mazouri FE, Sais L. Mining negatives association rules using constraints. Procedia Computer Science. 2018; 127(2018):481-8.

[45][45]Boudane A, Jabbour S, Sais L, Salhi Y. A sat-based approach for mining association rules. In proceedings of the international joint conference on artificial intelligence 2016 (pp. 2472-8). AAAI Press.