Association Rule mining plays an important role in the discovery of knowledge and information. Association Rule mining discovers huge number of rules for any dataset for different support and confidence values, among this many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant Association Rules in multi-level dataset is a big concern in field of Data mining. In this paper, we present a definition for redundancy and a concise representation called Reliable Exact basis for representing non-redundant Association Rules from multi-level datasets. The given non-redundant Association Rules are loss less representation for any datasets.
Multi agent is that the broader space that is developing at a fast pace towards analysis and development of distinct vary of topics. These areas agitate numerous methodologies towards the agent style and comprehensive classification theme. During this paper we tend to establish major mode aspects of software package agents, then provides an summary of existing ontologies, and combines the most effective aspects of those themes to propose a brand new classification scheme. So as an instance the classifications, the JACK Intelligent Agents design is delineate within the context of the theme.
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