article

Discovering high utility itemsets with multiple minimum supports

Authors:

Keun Ho RyuAuthors Info & Claims

Intelligent Data Analysis, Volume 18, Issue 6

Pages 1027 - 1047

Published: 01 November 2014 Publication History

Abstract

Generally, association rule mining uses only a single minimum support threshold for the whole database. This model implicitly assumes that all items in the database have the same nature. In real applications, however, each item can have different nature such as medical datasets which contain information of both diseases and symptoms or status related to the diseases. Therefore, association rule mining needs to consider multiple minimum supports. Association rule mining with multiple minimum supports discovers all item rules by reflecting their characteristics. Although this model can identify meaningful association rules including rare item rules, not only the importance of items such as fatality rate of diseases but also attribute of items such as duration of symptoms are not considered since it treats each item with equal importance and represents the occurrences of items in transactions as binary values. In this paper, we propose a novel tree structure, called MHU-Tree (Multiple item supports with High Utility Tree), which is constructed with a single scan. Moreover, we propose an algorithm, named MHU-Growth (Multiple item supports with High Utility Growth), for mining high utility itemsets with multiple minimum supports. Experimental results show that MHU-Growth outperforms the previous algorithm on both real and synthetic datasets, and can discover useful rules from a medical dataset.

References

[1]

R. Agrawal and R. Srikant, Fast algorithms for mining association rules, in: Proc of the 20th Int'l Conf, on Very Large Data Bases (VLDB 1994) (1994), 487-499.

[2]

C.F. Ahmed, S.K. Tanbeer, B.-S. Jeong, Y.-K. Lee and H.-J. Choi, Single-pass incremental and interactive mining for weighted frequent patterns, Expert Systems with Applications 39(9) (2012), 7976-7994.

[3]

C.F. Ahmed, S.K. Tanbeer, B.-S. Jeong and Y.-K. Lee, Efficient tree structures for high utility pattern mining in incremental databases, IEEE Transactions on Knowledge and Data Engineering 21(12) (2009), 1708-1721.

Digital Library

[4]

V.P. Álvarez and J.M. Vázquez, An evolutionary algorithm to discover quantitative association rules from huge databases without the need for an apriori discretization, Expert Systems with Applications 39(1) (2012), 2012.

[5]

R. Chaves, J. Ramírez, J.M. Górriz and C.G. Puntonet, Association rule-based feature selection method for alzheimer's disease diagnosis, Expert Systems with Applications 39(14) (2012), 11766-11774.

Digital Library

[6]

C.-H. Chen, T.-P. Hong and V.S. Tseng, An improved approach to find membership dunctions and multiple minimum supports in fuzzy data mining, Expert Systems with Applications 36(6) (2009), 10016-10024.

Digital Library

[7]

C.-H. Chen, T.-P. Hong and V.S. Tseng, Genetic-fuzzy mining with multiple minimum supports based on fuzzy clustering, Soft Computing 15(12) (2011), 2319-2333.

Digital Library

[8]

S.-S. Chen, T.C.-K. Huang and Z.-M. Lin, New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports, Journal of Systems and Software 84(10) 1638-1651.

Digital Library

[9]

J. Han and Y. Fu, Discovery of multiple-level association rules from large databases, in: Proc of the 21th Int'l Conf on Very Large Database (VLDB 1995) (1995), 420-431.

[10]

J. Han, J. Pei and Y. Yin, Mining frequent patterns without candidate generation, in: Proc of the 2000 ACM SIGMOD Int'l Conf on Management of Data (2000), 1-12.

[11]

T.C.-K. Huang, Discovery of fuzzy quantitative sequential patterns with multiple minimum supports and adjustable membership functions, Information Sciences 222 (2013), 126-146.

Digital Library

[12]

Y.-H. Hu, F. Wu and Y.-J. Liao, An efficient tree-based algorithm for mining sequential patterns with multiple minimum supports, Journal of Systems and Software 86(5) (2013), 1224-1238.

Digital Library

[13]

Y.-H. Hu and Y.-L. Chen, Mining association rules with multiple minimum supports: A new mining algorithm and a support tuning mechanism, Decision Support Systems 42(1) (2006), 1-24.

Digital Library

[14]

R.U. Kiran and P.K. Reddy, Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms, The 14th Int'l Conf on Extending Database Technology (EDBT 2011) (2001), 11-20.

[15]

W. Lee, S.J. Stolfo and K.W. Mok, Mining audit data to build intrusion detection models, in: Proc the 4th Int'l Conf on Knowledge Discovery and Data Mining (KDD 1998) (1998), 66-72.

[16]

G. Lee, U. Yun and K. Ryu, Sliding window based weighted maximal frequent pattern mining over data streams, Expert Systems with Applications 41(2) (Feb 2014), 694-708.

Digital Library

[17]

Y.-C. Lee, T.-P. Hong and T.-C. Wang, Multi-level fuzzy mining with multiple minimum supports, Expert Systems with Applications 34(1) (2008), 459-468.

Digital Library

[18]

C.-W. Lin, G.-C. Lan and T.-P. Hong, An incremental mining algorithm for high utility itemsets, Expert Systems with Applications 39(8) (2012), 7173-7180.

[19]

C.-W. Lin, T.-P. Hong and W.-H. Lu, An effective tree structure for mining high utility itemsets, Expert Systems with Applications 38(6) (2011), 7419-7424.

Digital Library

[20]

X.-B. Liu, K. Zhai and W. Pedrycz, An improved association rules mining method, Expert Systems with Applications 39(1) (2012), 1362-1374.

Digital Library

[21]

Y.-C. Liu, C.-P. Cheng and V.S. Tseng, Discovering relational-based association rules with multiple minimum supports on microarray datasets, Bioinformatics 27(22) (2011), 3142-3148.

Digital Library

[22]

B. Liu, W. Hsu and Y. Ma, Mining association rules with multiple minimum supports, in: Proc of the Fifth ACM SIGKDD Int'l Conf on Knowledge Discovery and Data Mining (KDD 1999) (1999), 337-341.

Digital Library

[23]

Y. Liu, W.-K. Liao and A.N. Choudhary, A two-phase algorithm for fast discovery of high utility itemsets, Advances in Knowledge Discovery and Data Mining (PAKDD 2005) (2005), 689-695.

[24]

H. Mannila, Database methods for data mining, in: ACM SIGKDD Conf on Knowledge Discovery and Data Mining (KDD 1998) Tutorial, 1998.

[25]

S.B. Patil and V.S. Kumaraswamy, Intelligent and effective heart attack prediction system using data mining and artificial neural network, European Journal of Scientific Research 31(4) (2009), 642-656.

[26]

G. Pyun, U. Yun and K. Ryu, Efficient frequent pattern mining based on linear prefix tree, Knowledge-Based Systems 55 (Jan 2014), 125-139.

[27]

M. Shinoda, T. Ozaki and T. Ohkawa, Weighted frequent subgraph mining in weighted graph databases, IEEE Int'l Conf on Data Mining Workshops (ICDM 2009) (2009), 58-63.

[28]

V.S. Tseng, B.-E. Shie, C.-W. Wu and P.S. Yu, Efficient algorithms for mining high utility itemsets from transactional databases, IEEE Transactions on Knowledge and Data Engineering 25(8) (2013), 1772-1786.

Digital Library

[29]

C.-W. Wu, P. Fournier-Viger, P.S. Yu and V.S. Tseng, Efficient mining of a concise and lossless representation of high utility itemsets, The 11th IEEE Int'l Conf on Data Mining (ICDM 2011) (2011), 824-833.

[30]

U. Yun, G. Lee and K. Ryu, Mining maximal frequent patterns by considering weight conditions over data streams, Knowledge-Based Systems 55 (Jan 2014), 49-65.

Digital Library

[31]

U. Yun, H. Ryang and K. Ryu, High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates, Expert Systems with Applications 41(8) (June 2014), 3861-3878.

Digital Library

[32]

U. Yun and K. Ryu, Efficient mining of maximal correlated weight frequent patterns, Intelligent Data Analysis 17(5) (Sep 2013), 917-939.

Digital Library

Cited By

Singh KKumar RBiswas B(2022)High average-utility itemsets mining: a surveyApplied Intelligence10.1007/s10489-021-02611-z52:4(3901-3938)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10489-021-02611-z
Gan WLin JFournier-Viger PChao HYu P(2021)Beyond FrequencyACM Transactions on Internet Technology10.1145/342549821:1(1-32)Online publication date: 5-Jan-2021
https://dl.acm.org/doi/10.1145/3425498
Darrab SErgenc B(2017)Vertical Pattern Mining Algorithm for Multiple Support ThresholdsProcedia Computer Science10.1016/j.procs.2017.08.051112:C(417-426)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.procs.2017.08.051
Show More Cited By

Discovering high utility itemsets with multiple minimum supports
1. Information systems
  1. Information systems applications

Recommendations

Mining high average utility itemsets using artificial fish swarm algorithm with computed multiple minimum average utility thresholds

It is obvious that the problem of Frequent Itemset Mining (FIM) is very popular in data mining, which generates frequent itemsets from a transaction database. An extension of the frequent itemset mining is High Utility Itemset Mining (HUIM) which ...
Mining Minimal High-Utility Itemsets
DEXA 2016: Proceedings, Part I, 27th International Conference on Database and Expert Systems Applications - Volume 9827

Mining high-utility itemsets HUIs is a key data mining task. It consists of discovering groups of items that yield a high profit in transaction databases. A major drawback of traditional high-utility itemset mining algorithms is that they can return a ...
High average-utility itemsets mining: a survey
Abstract
HUIM (High utility itemsets mining) is a sub-division of data mining dealing with the task to obtain promising patterns in the quantitative datasets. A variant of HUIM is to discover the HAUIM (High average-utility itemsets mining) where average-...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Intelligent Data Analysis

Intelligent Data Analysis Volume 18, Issue 6

November 2014

238 pages

ISSN:1088-467X

Issue’s Table of Contents

Publisher

IOS Press

Netherlands

Publication History

Published: 01 November 2014

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Singh KKumar RBiswas B(2022)High average-utility itemsets mining: a surveyApplied Intelligence10.1007/s10489-021-02611-z52:4(3901-3938)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10489-021-02611-z
Gan WLin JFournier-Viger PChao HYu P(2021)Beyond FrequencyACM Transactions on Internet Technology10.1145/342549821:1(1-32)Online publication date: 5-Jan-2021
https://dl.acm.org/doi/10.1145/3425498
Darrab SErgenc B(2017)Vertical Pattern Mining Algorithm for Multiple Support ThresholdsProcedia Computer Science10.1016/j.procs.2017.08.051112:C(417-426)Online publication date: 1-Sep-2017
https://dl.acm.org/doi/10.1016/j.procs.2017.08.051
Ryang HYun U(2017)Indexed list-based high utility pattern mining with utility upper-bound reduction and pattern combination techniquesKnowledge and Information Systems10.1007/s10115-016-0989-x51:2(627-659)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1007/s10115-016-0989-x
Yun UKim DRyang HLee GLee K(2016)Mining recent high average utility patterns based on sliding window from stream dataJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/IFS-16210630:6(3605-3617)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.3233/IFS-162106
Jeyabharathi JShanthi D(2016)Enhanced sequence identification technique for protein sequence database mining with hybrid frequent pattern mining algorithmInternational Journal of Data Mining and Bioinformatics10.1504/IJDMB.2016.08067316:3(205-229)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1504/IJDMB.2016.080673
Yun ULee GLee K(2016)Efficient representative pattern mining based on weight and maximality conditionsExpert Systems: The Journal of Knowledge Engineering10.1111/exsy.1215833:5(439-462)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1111/exsy.12158
Tseng VWu CFournier-Viger PYu P(2016)Efficient Algorithms for Mining Top-K High Utility ItemsetsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.245886028:1(54-67)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1109/TKDE.2015.2458860
Lin JGan WFournier-Viger PHong TZhan J(2016)Efficient mining of high-utility itemsets using multiple minimum utility thresholdsKnowledge-Based Systems10.1016/j.knosys.2016.09.013113:C(100-115)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.knosys.2016.09.013
(2016)Sliding window based weighted erasable stream pattern mining for stream data applicationsFuture Generation Computer Systems10.1016/j.future.2015.12.01259:C(1-20)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1016/j.future.2015.12.012
Show More Cited By

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents