Inducing rules from very large datasets is one of the most challenging areas in data mining. Several approaches exist to scaling up classification rule induction to large datasets, namely data reduction and the parallelisation of classification rule induction algorithms. In the area of parallelisation of classification rule induction algorithms most of the work has been concentrated on the Top ...

Pre Prospection data mining model for hydrocarbon development guides to the need of developing a model for prospection activity for which the data of surface indicators collected through remote sensing and microbial sources can be used. This chapter proposes a new approach to mine context based positive and negative spatial association rules. Researchers are using Apriori algorithm on spatial d...

The task of mining the association rule has become one most widely used discovery pattern methods in Knowledge Discovery Databases (KDD). One such is to represent itemset memory. representation largely depend on type data structure that for storing them. Computing process im- pacts memory and time requirement itemset. With increase dimensionality datasets, large volume datasets will be difficul...

     Data reduction is used to aggregate or amalgamate the large data sets into smaller and manageable information pieces in order to fast and accurate classification of different attributes. However, excessive spatial or spectral data reduction may result in losing or masking important radiometric information. Therefore, we conducted this research to evaluate the effectiveness of the different...

Based on practical observations on rule-based inference on RDF data, we study the problem of redundancy elimination on RDF graphs in the presence of rules (in the form of Datalog rules) and constraints, (in the form of so-called tuple-generating dependencies), and with respect to queries (ranging from conjunctive queries up to more complex ones, particularly covering features of SPARQL, such as...

a parallel algorithm for generating t-ary tree sequences in reverse b-order is presented. the algorithm generates t-ary trees by 0-1 sequences, and each 0-1 sequences is generated in constant average time o(1). the algorithm is executed on a crew sm simd model, and is adaptive and cost-optimal. prior to the discussion of the parallel algorithm a new sequential generation with o(1) average time ...