Attribute oriented induction with star schema

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.Comment: 23 Pages, IJDM

Attribute Oriented Induction High Level Emerging Pattern (AOI-HEP)

Attribute-Oriented Induction of High-level Emerging Pattern(AOI-HEP) is a combination of Attribute Oriented Induction (AOI) and Emerging Patterns (EP). AOI is a summarisation algorithm that compact a given dataset into small conceptual descriptions, where each attribute has a defined concept hierarchy. This presents patterns are easily readable and understandable.Emerging patterns are patterns discovered between two datasets and between two time periods such that patterns found in the first dataset have either grown (or reduced) in size, totally disappeared or new ones have emerged. AOI-HEP is not influenced by border-based algorithm like in EP mining algorithms. It is desirable therefore that we obtain summarised emerging patterns between two datasets. We propose High-level Emerging Pattern (HEP) algorithm. The main purpose of combining AOI and EP is to use the typical strength of AOI and EP to extract important high-level emerging patterns from data. The AOI characteristic rule algorithm was run twice with two input datasets,to create two rulesets which are then processed with the HEP algorithm. Firstly, the HEP algorithm starts with cartesian product between two rulesets which eliminates rules in rulesets by computing similarity metric (a categorization of attribute comparisons). Secondly, the output rules between two rulesets from the metric similarity are discriminated by computing a growth rate value to find ratio of supports between rules from two rulesets. The categorization of attribute comparisons is based on similarity hierarchy level. The categorisation of attributes was found to be with three options in how they subsume each other. These were Total Subsumption HEP (TSHEP), Subsumption Overlapping HEP (SOHEP) and Total Overlapping HEP (TOHEP) patterns. Meanwhile, from certain similarity hierarchy level and values, we can mine frequent and similar patterns that create discriminant rules. We used four large real datasets from UCI machine learning repository and discovered valuable HEP patterns including strong discriminant rules, frequent and similar patterns. Moreover, the experiments showed that most datasets have SOHEP but not TSHEP and TOHEP and the most rarely found were TOHEP. Since AOI- iii HEP can strongly discriminate high-level data, assuredly AOI-HEP can be implemented to discriminate datasets such as finding bad and good customers for banking loan systems or credit card applicants etc. Moreover, AOI-HEP can be implemented to mine similar patterns, for instance, mining similar customer loan patterns etc

Uso de tecnicas de classificação automatica na analise ambiental : um estudo de caso

Orientador : Luiz Henrique Antunes RodriguesDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia AgricolaMestrad