Enhancing Quality of Knowledge Synthesized from Multi-database Mining
Multi-database mining using local pattern analysis could be considered as an approximate method of mining multiple large databases. Assuming this point of view, it might be required to enhance the quality of knowledge synthesized from multiple databases.
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Enhancing Quality of Knowledge Synthesized from Multi-database Mining
Multi-database mining using local pattern analysis could be considered as an approximate method of mining multiple large databases. Assuming this point of view, it might be required to enhance the quality of knowledge synthesized from multiple databases. Also, many decision-making applications are directly based on the available local patterns present in different databases. The quality of synthesized knowledge/decision based on local patterns present in different databases could be enhanced by incorporating more local patterns in the knowledge synthesizing/processing activities. Thus, the available local patterns play a crucial role in building efficient multi-database mining applications. We represent patterns in a condensed form by employing a so-called ACP (antecedent-consequent pair) coding. It allows one to consider more local patterns by lowering further the user-defined characteristics of discovered patterns, like minimum support and minimum confidence. The ACP coding enables more local patterns participate in the knowledge synthesizing/processing activities and thus the quality of synthesized knowledge based on local patterns becomes enhanced significantly with regard to the synthesizing algorithm and required computing resources. To secure a convenient access to association rule, we introduce an index structure. We demonstrate that ACP coding represents rulebases by making use of the least amount of storage space in comparison to any other rulebase representation technique. Furthermore we present a technique for storing rulebases in the secondary storage.
5.1 Introduction In Chapters 2, 3, and 4, we have discussed how to improve multi-database mining by adopting different mining techniques. Also, we have learnt that a single multidatabase mining technique might not be sufficient in all situations. Chapters 2 and 3 present different variations of multi-database mining using local pattern analysis. Multi-database mining using local pattern analysis could be considered as an approximate method of mining multiple large databases. In this chapter, we employ a coding, referred to as antecedent-consequent pair (ACP) coding, to improve the quality of synthesized knowledge coming from multi-database mining. The ACP coding enables an efficient storage for association rules in multiple databases space. A. Adhikari et al., Developing Multi-database Mining Applications, Advanced Information and Knowledge Processing, DOI 10.1007/978-1-84996-044-1_5, C Springer-Verlag London Limited 2010
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5 Enhancing Quality of Knowledge Synthesized from Multi-database Mining
One could extract knowledge of better quality by storing more association rules in the main memory. In this way, applications dealing with association rules in multiple databases become more efficient. Consider a multi-branch company that operates at different locations. Each branch generates a large database and subsequently we have to deal with multiple large databases. In particu
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