Colin L. Carter

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We propose the share-conndence framework for knowledge discovery from databases which addresses the problem of mining item-sets from market basket data. Our goal is twofold: (1) to present new itemset measures which are practical and useful alternatives to the commonly used support measure; (2) to not only discover the buying patterns of customers, but also(More)
We propose the share-conndence framework for knowledge discovery from databases which addresses the problem of mining characterized association rules from market basket data (i.e., itemsets). Our goal is to not only discover the buying patterns of customers, but also to discover customer prooles by partitioning customers into distinct classes. We present a(More)
We present a O(n) algorithm for generalizing a database relation using concept hierarchies, where n is the number of tuples in the input relation. The algorithm is based on a variant of Han et al.'s attribute-oriented O(n log n) algorithm. Our algorithm is an on-line algorithm; fast performance is achieved because after encountering a tuple and generalizing(More)
Practical tools for knowledge discovery from databases must be efficient enough to handle large data sets found in commercial environments. Attribute-oriented induction has proved to be a useful method for knowledge discovery, but two algorithms which implement it, AOI and LCHR, have some limitations due to excessive memory usage. AOI and LCHR generalize(More)
We present GDBR, Generalize DataBase Relation, an optimal, on-line O(n) algorithm for database relation generalization using concept hierarchies. The algorithm is a variant of attribute-oriented induction which generalizes database relations in either O(n log n) or O(np) time, where n is the number of input tuples and p is the number of tuples in the output(More)
We introduce the measures share, coincidence and dominance as alternatives to the standard itemset methodology measure of support. We also redefine the confidence measure in this context. An itemset is a group of items bought together in a transaction. The support of an itemset is the ratio of transactions in which an itemset appears to the total number of(More)