Survey on Attribute Oriented Induction Using Data Mining Techniques
Keywords:
AOI, Clustering, Data mining, generalizationAbstract
Data and objects in databases often contain detailed information at primitive concept levels. It is useful to summarize a large set of data and present it at a high conceptual level. Attribute Oriented Induction(AOI) is a set-oriented data base mining method which generalizes the task-relevant subset of data attribute-by-attribute compresses it into a generalized relation and extracts from it the general features of data. The power of AOI is extraction from relational databases of different kinds of patterns including characteristic rules, discriminant rules, cluster description rules and multilevel association rules. The method is efficient, robust with wide applications and extensible to knowledge discovery in advanced database systems, including object-oriented, deductive and spatial database systems. This paper describes the broad classification of data mining techniques using AOI.
References
David Wai-lok Cheung, Ada Wai-Chee Fu and Jiawei Han “A Rule-Based Attribute-Oriented Approach”, Knowledge Discovery in Databases.
M.V. Jagannatha Reddy, B.Kavitha, “Extracting prediction rules for loan default using neural networks through Attribute
Relevance Analysis”,International Journal of Computer Theory and Engineering, Vokume 2,No.4,August 2010,1793-8201.
Yu-Ying Wu., Yen-Liand Chen., and Ray-I Chang, “Generalized Knowledge discovery from Relational Databases”, International Journal of Computer Sciences and Network Security, Vol.9 No.6,June 2009.
C.L.Carter and H.J.Hamilton, “Efficient attribute-oriented generalization for knowledge discovery from large databases”, IEEE Transactions on Knowledge and Data Engineering 10(2)(1998)193-208.
C. Hsu,” Extending attribute-oriented induction algorithm for major values and numeric values”, Expert Systems with Applications 27(2)(2004)187-202.
Y.L.Chen and C.C Shen, “Mining generalized knowledge from ordered data through attribute-oriented induction techniques”, European Journal of Operational Research 166(1)(2005)221-45.
A.Savasere, E.Omiecinski and S.Navathe,” Mining for Strong Negative Associations in a Large Database of Customer Transactions”, Proceedings of the Fourteenth International Conference on Data Engineering.(1998).
Qingshyang Jiang, Syed Sibte Raza Abidi,” A Hybrid of Conceptual Clusters, Rough Sets and Attribute Oriented Induction For Inducing Symbolic Rules”.
Spits Warnars H.L.H., “Attribute Oriented Induction with Star Schema”, International Journal of Database Management Systems(IJDMS)”, Vol.2,No.2,May 2010.
Jiawei Han., Ywandong Cai and Nick Cercone., “Knowledge Discovery in Databases: An Attribute-oriented Approach”; Proceedings of the 18th VLDB Conference;1992.
Hoi-Yee Hwang and Wai-Chee Fu.,” Efficient Algorithms for Attribute-Oriented Induction”; KDD Proceedings;1995.
Shu-Meng Huang., Ping-Yu Hsu., Hwynh Ngynh NguYen Nhat Lam, An Attribute-Oreinted Approach for knowledge Discovery from Relational Databases”, Advances in information Sciences and Service Sciences (AISS) Volume5,Number3, Feb 2013.
Lukas Tanutama.,” Frequency Count Attribute Oriented Induction of Corporate Network Data for Mapping Business Activity” ;EPJ Web of Conferences;2014.
Son Dao., Brad Perry.,” Applying a Data Miner to Heterogeneous Schema Integration”, KDD Proceedings,1995,63-68.
Jen-Yin Yeh., Shen-Tsu Wang., Chien-Hsin Lin., “Explore Financial Data Characteristics of Different Types of Enterprises During Rise in Stock Prices, Using a Semantic Attribute-Oriented Induction Algorithm”; 2012 International workshop on Information and Electronics Engineering (IWIEE).
Spits Warnars.,” Mining Frequent and Similar Patterns with Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP) Data Mining Technique”, International Journal of Emerging Technologies in computational and Applied Sciences(IJETCAS), 266-276,2015.
Victor C.Cheng., C.H.C.Leung, Jiming Liu, Alfredo Milani ; “Probabilistic Aspect Mining Model for Drug Reviews”; IEEE
Transactions on Knowledge and Data Engineering, Vol,26,No.8,Aug 2014.
Rafal A.Angryl, Jacck Czerniak, “Heuristic algorithm for interpretation of multi-valued attributes in similarity-based fuzzy relational databases”, International Journal of Approximate Reasoning,2010.
J.Isabella and Dr.R.M.Suresh,”Analysis and evaluation of feature selectors in opinion mining”, International Journal of Computer Science and Engineering”,Vol.3,No.6,Dec 2012-jan 2013.
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