Domain-concept mining: an efficient on-demand data mining approach
Mahamaneerat, Wannapa Kay, 1974-
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Traditional brute-force association mining approaches, when applied to large datasets, are thorough but inefficient due to computational complexity. A low global minimum probability threshold can worsen this complexity by producing an overwhelming number of associations; however, a high threshold may not uncover valuable associations, especially from underrepresented groups within the population. Regardless, the uncovered associations are not systematically organized. To solve these problems, novel Domain-Concept Mining (DCM) with Partition Aggregation (DCM-PA) has been developed. DCM organizes data by grouping transactions with common characteristics, such as a certain age group, into "domain-concepts" (dc). DCM granulizes partitioning criteria by pairing each attribute with its values. Criteria may include under-represented groups as well as spatial, temporal, and incremental dimensionalities. Then, a statistical power analysis is utilized to determine if multiple criteria of the same attribute, such as age group 18-24 and 25-34, should be combined to form a broader partition. Doing so maintains the tradeoff between findings with statistical significance and computational resource consumptions, while preserving data organization. Associations can be extracted from each partition independently because a partition contains all of its qualified transactions. Moreover, the partition size proportionally adjusts the global threshold to be more specific and sensitive. After the initial phase is complete, DCM-PA efficiently reuses DCM's associations to compute results from multiple-partition aggregation (union or intersection) using Bayes Theorem and a pipelining technique. DCM-PA offers the flexibility to perform association mining that is expected to uncovering more valuable knowledge through means like trends and comparisons from various dc partitions and their aggregations.
Computer science (MU)
2008 Freely available dissertations (MU)