Attribute Oriented Induction

Classification is widely used technique in the data mining domain, where scalability and efficiency are the immediate problems in classification algorithms for large databases. We suggest improvements to the existing C4.5 decision tree algorithm. In this paper attribute oriented induction (AOI) and relevance analysis are incorporated with concept hierarchy's knowledge and HeightBalancePriority algorithm for construction of decision tree along with Multi level mining. The assignment of priorities to attributes is done by evaluating information entropy, at different levels of abstraction for building decision tree using HeightBalancePriority algorithm. Modified DMQL queries are used to understand and explore the shortcomings of the decision trees generated by C4.5 classifier for education dataset and the results are compared with the proposed approach.

Mining data in human activity life such as business, education, engineering, health and so on, is important and help human itself in order to justify their decision making process. Attribute Oriented Induction (AOI) has been using to mine significant different patterns since was coined in 1989, has been combined and as complement with other data mining pattern. AOI has been proved and powerful, has future opportunity to be explored in order to help human life to find data patterns. AOI is chosen since can reduce many patterns by summarize/roll up many patterns in low into high level in concept tree/hierarchy. However, non summarize pattern at low level in concept tree/hierarchy can be used to sharpen the mining knowledge pattern just as like roll up and drill down in data warehouse. Mapping implementation of AOI in human life area such as business, education, engineering, health and so on, is useful in order to give valuable knowledge AOI mining pattern, particularly for those who interest with AOI data mining technique as data mining technique which can summarize many pattern into simple patterns.

Classification is widely used technique in the data mining domain, where scalability and efficiency are the immediate problems in classification algorithms for large databases. We suggest improvements to the existing C4.5 decision tree algorithm. In this paper attribute oriented induction (AOI) and relevance analysis are incorporated with concept hierarchy's knowledge and HeightBalancePriority algorithm for construction of decision tree along with Multi level mining. The assignment of priorities to attributes is done by evaluating information entropy, at different levels of abstraction for building decision tree using HeightBalancePriority algorithm. Modified DMQL queries are used to understand and explore the shortcomings of the decision trees generated by C4.5 classifier for education dataset and the results are compared with the proposed approach.

This paper will propose a novel star schema attribute induction as a new attribute induction paradigm and as improving from current attribute oriented induction. A novel star schema attribute induction will be examined with current attribute oriented induction based on characteristic rule and using non rule based concept hierarchy by implementing both of approaches. In novel star schema attribute induction some improvements have been implemented like elimination threshold number as maximum tuples control for generalization result, there is no ANY as the most general concept, replacement the role concept hierarchy with concept tree, simplification for the generalization strategy steps and elimination attribute oriented induction algorithm. Novel star schema attribute induction is more powerful than the current attribute oriented induction since can produce small number final generalization tuples and there is no ANY in the results.