Abstract

Many real world data are closely associated with intervals. Mining frequent intervals from such data allows us to group those data depending on some similarity. A few numbers of data mining approaches have been developed to discover frequent intervals from interval datasets. Here we present a complementary approach in which we search for sparse intervals in data. We present an efficient algorithm with a worst case time complexity of O(n log n) for mining maximal sparse intervals.

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