An Efficient Mechanism for Data Mining Based On Item Sets Features
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Frequent pattern mining casts a vital part in many of significant data mining tasks such as associations, sequential patterns, partial periodicity, to name a few. Nevertheless, it is common knowledge that frequent pattern mining habitually produces an extreme number of frequent item sets and rules, paving the way for reducing in competence as well as efficiency of extraction in view of the fact that clients are best with the task of sieving through a huge number of extracted rules to locate the fruitful ones. Therefore, without resorting to the extraction of the frequent item sets, mining only...
Frequent pattern mining casts a vital part in many of significant data mining tasks such as associations, sequential patterns, partial periodicity, to name a few. Nevertheless, it is common knowledge that frequent pattern mining habitually produces an extreme number of frequent item sets and rules, paving the way for reducing in competence as well as efficiency of extraction in view of the fact that clients are best with the task of sieving through a huge number of extracted rules to locate the fruitful ones. Therefore, without resorting to the extraction of the frequent item sets, mining only closed frequent item sets goes a long way in incredibly enhancing the excellence along with the decrease in the computational time. This book focuses on a new mechanism for implementing the frequent pattern mining.
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