document: Maximally informative k-itemsets and their efficient discovery In this paper we present a new approach to mining binary data . We treat each binary feature ( item ) as a means of distinguishing two sets of examples . Our interest is in selecting from the total set of items an itemset of specified size , such that the database is partitioned with as uniform a distribution over the parts as possible . To achieve this goal , we propose the use of joint entropy as a quality measure for itemsets , and refer to optimal itemsets of cardinality k as maximally informative k-itemsets . We claim that this approach maximises distinctive power , as well as minimises redundancy within the feature set . A number of algorithms is presented for computing optimal itemsets efficiently .
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