A Survey on Hesitation Information Mining

tremendous advancement in technology has given rise to an increasing requirement for the storage of data in files, databases and other data repositories. As a result, decision- makers are required to use new and powerful automated tools for the purpose of the analysis and interpretation of the stored data, as well as for the extraction of interesting patterns in data. Since the stored data is not always exact and precise, some means are required to handle this aspect of data and extract the useful information (e.g. hesitation information) arising from such uncertainties. For this purpose vague set theory has been applied for efficiently modeling the uncertainties that occur in datasets. Vague sets are an extension of the classical set theory. They extend the application of set theory to vague and uncertain problems. Vague set theory has emerged as a new tool to deal with the uncertainties of the data and the parameters attached to the data. This theory exhibits a very promising approach to analyzing uncertain data and deriving some interesting results suitable for use in various applications. This paper discusses the notions of vague sets and vague association rules. Also, optimization techniques have been introduced that would be helpful in optimizing the outcomes. When both are implemented together, a new approach will be created which is expected to generate much improved results. This paper emphasizes on the study of vague sets for a series of applications especially in decision making problems.

[1]  Wilfred Ng,et al.  Mining Hesitation Information by Vague Association Rules , 2007, ER.

[2]  A. Rosenfeld,et al.  IEEE TRANSACTIONS ON SYSTEMS , MAN , AND CYBERNETICS , 2022 .

[3]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[4]  Jian Pei,et al.  Data Mining: Concepts and Techniques, 3rd edition , 2006 .

[5]  Xiaohui Yan,et al.  A new approach for data clustering using hybrid artificial bee colony algorithm , 2012, Neurocomputing.

[6]  G. S. Thakur,et al.  Vague Set Theory for Profit Pattern and Decision Making in Uncertain Data , 2015 .

[7]  Feifei Li,et al.  Finding frequent items in probabilistic data , 2008, SIGMOD Conference.

[8]  Wilfred Ng,et al.  Mining Vague Association Rules , 2007, DASFAA.

[9]  D. Molodtsov.  Soft set theory—First results , 1999 .

[10]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[11]  Hans-Peter Kriegel,et al.  Probabilistic frequent itemset mining in uncertain databases , 2009, KDD.

[12]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[13]  Bala Yesu Chilakalapudi,et al.  An Improved Algorithm for Efficient Mining of Frequent Item Sets on Large Uncertain Databases , 2013 .

[14]  Michael Stonebraker,et al.  The Morgan Kaufmann Series in Data Management Systems , 1999 .

[15]  Dantong Ouyang,et al.  An artificial bee colony approach for clustering , 2010, Expert Syst. Appl..

[16]  Supriya Raheja,et al.  A Vague Improved Markov Model Approach for Web Page Prediction , 2014, ArXiv.

[17]  Dervis Karaboga,et al.  A novel clustering approach: Artificial Bee Colony (ABC) algorithm , 2011, Appl. Soft Comput..

[18]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[19]  W.-L. Gau,et al.  Vague sets , 1993, IEEE Trans. Syst. Man Cybern..

[20]  Wilfred Ng,et al.  Vague Sets or Intuitionistic Fuzzy Sets for Handling Vague Data: Which One Is Better? , 2005, ER.

[21]  Dervis Karaboga,et al.  Artificial bee colony programming for symbolic regression , 2012, Inf. Sci..

[22]  Minghao Yin,et al.  Animal migration optimization: an optimization algorithm inspired by animal migration behavior , 2014, Neural Computing and Applications.

[23]  Anjana Pandey,et al.  A Model for Mining Course Information using Vague Association Rule , 2012 .

[24]  Wilfred Ng,et al.  Managing Merged Data by Vague Functional Dependencies , 2004, ER.

[25]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[26]  Wilfred Ng,et al.  Handling Inconsistency of Vague Relations with Functional Dependencies , 2007, ER.

[27]  K. R. Pardasani,et al.  A Model for Vague Association Rule Mining in Temporal Databases , 2013 .