Titlebook: Association Rule Mining; Models and Algorithm Chengqi Zhang,Shichao Zhang Textbook 2002 Springer-Verlag Berlin Heidelberg 2002 Algorithmic

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Association Rules in Very Large Databases,g., with terabytes of data) to be processed at one time. An ideal way of mining very large databases would be by us- ing paralleling techniques. This system employs hardware technology, such as parallel machines, to implement concurrent data mining al- gorithms. However, parallel machines are expens

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Conclusion and Future Work, issues that need to be explored for identifying useful association rules. In this chapter, these issues are outlined as possible future problems to be solved. In Section 8.1, we summarize the previous seven chapters. And then, in Section 8.2, we describe four other challenging problems in associati

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Association Rules in Very Large Databases,system employs hardware technology, such as parallel machines, to implement concurrent data mining al- gorithms. However, parallel machines are expensive, and less widely available, than single processor machines. This chapter presents some techniques for mining association rules in very large databases, using instance selection.

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Lecture Notes in Computer Science3, we introduce the Apriori algorithm. This algorithm searches large (or frequent) itemsets in databases. Section 2.4 introduces some research into mining association rules. Finally, we summarize this chapter in Section 2.5.

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https://doi.org/10.1007/978-3-319-39570-8re are essential differences between positive and negative association rule mining. Using a pruning algo- rithm we can reduce the search space, however, some pruned itemsets may be useful in the extraction of negative rules.

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Multiple Mutation Testing from FSM,onstructing polynomial functions for approximate causality in data are advocated. Finally, we propose an approach for finding the approximate polynomial causal- ity between two variables from a given data set by fitting.