Data mining association rules

R. Agrawal, T. Imielinski and A. S wami, Mining association rules between sets of items in large databases, in Proceedings of the International Conference on Management of Data, ACM Press, New York, 1993. 

The rules to predict AlP04-5 (AFI) synthesis are preliminarily built by data mining. The results associate with six attributes, involving the longest atomic distance > 0.496 nm, the secondary distance < 0.765 nm, the ratio of the number of protons acceptable by the template to the number of N atoms < 8, and the formation enthalpy <421.41 kJ/mol. The reliability of the constraint is 178/190 = 93.7%, and the support-ability of that is 190/549 = 34.6%. 

Association rules are among the most popular representations for local structures or patterns in data mining [3]. These patterns are inferred without prior knowledge of predefined classes. A rule consists of a left-hand side proposition called the antecedent and a right-hand side called the consequent. A typical example rule is shown below [14] ... 

Another type of dependency is that which results from some sort of causal mechanism. Such causality is often represented in Data Mining by using Bayesian Belief Networks which discover and describe. Such causal models allow us to predict consequences, even when circumstances change. If a rule just describes an association, then we cannot be sure how robust or generalizable it will be in the face of changing circumstances. 

An important use of fuzzy methods for Data Mining is for classification. Associations between inputs and outputs are known in fuzzy systems as fuzzy associative memories or FAMs. A FAM system encodes a collection of compound rules that associate multiple input statements with multiple output statements We combine such multiple statements using logical operators such as conjunction, disjunction and negation. 

For Data Mining purposes it is often necessary to use a large number of data manipulations of various types. When searching for Association Rules, for example, tuples... 

Temporal Data Mining often involves processing time series, typically sequences of data, which measure values of the same attribute at a sequence of different time points. Pattern matching using such data, where we are searching for particular patterns of interest, has attracted considerable interest in recent years. In addition to traditional statistical methods for time series analysis, more recent work on sequence processing has used association rules developed by the database community. In addition Temporal Data Mining may involve exploitation of efficient... 

Yang, Q. (2002). Building Association Rule-Based Sequential Classifiers for Web Document Prediction. Journal of Data Mining and Knowledge Discovery. 

Agrawal, R., H. Mannila, R. Srikant, H. Toivonen and A.I. Verkamo, 1996. Fast discovery of association rules. In Advances in knowledge discovery and data mining. U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy (eds.), MIT Press, pp. 307-328. 

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