Hierarchical Approach for Online Mining--Emphasis towards Software Metrics

Several multi-pass algorithms have been proposed for Association Rule Mining from static repositories. However, such algorithms are incapable of online processing of transaction streams. In this paper we introduce an efficient single-pass algorithm for mining association rules, given a hierarchical classification amongest items. Processing efficiency is achieved by utilizing two optimizations, hierarchy aware counting and transaction reduction, which become possible in the context of hierarchical classification. This paper considers the problem of integrating constraints that are Boolean expression over the presence or absence of items into the association discovery algorithm. This paper present three integrated algorithms for mining association rules with item constraints and discuss their tradeoffs. It is concluded that the variation of complexity depends on the measure of DIT (Depth of Inheritance Tree) and NOC (Number of Children) in the context of Hierarchical Classification.Comment: Pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 7 No. 2, February 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis

Benchmark Algorithms and Models of Frequent Itemset Mining over Data Streams: Contemporary Affirmation of State of Art

Data mining and knowledge discovery is an active research work and getting popular by the day because it can be applied in different type of data like web click streams, sensor networks, stock exchange data and time-series data and so on. Data streams are not devoid of research problems. This is attributed to non-stop data arrival in numerous, swift, varying with time, erratic and unrestricted data field. It is highly important to find the regular prototype in single pass data stream or minor number of passes when making use of limited space of memory. In this survey the review on the final progress in the study of regular model mining in data streams. Mining algorithms are talked about at length and further research directions have been suggested

An Efficient Algorithm for Hierarchical Online Mining of Association Rules

Several multi-pass algorithms have been proposed for Association Rule Mining from static repositories. However, such algorithms are incapable of online processing of transaction streams. In this paper we introduce an efficient single-pass algorithm for mining association rules, given a hierarchical classification amongst items. Processing efficiency is achieved by utilizing two optimizations, hierarchy aware counting and transaction reduction, which become possible in the context of hierarchical classification. We also propose a modified algorithm for the rule generation phase which avoids the construction of an explicit adjacency lattice