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Intervals are found in many real life applications such as web uses; stock market information; patient disease records; records maintained for occurrences of events, either man made or natural etc. Mining frequent intervals from such data allow one to group the transactions with similar behavior. Similar to frequent intervals, mining sparse intervals are also important. In this paper, the authors define the notion of sparse and maximal sparse interval and also propose an algorithm for mining maximal sparse intervals. Computer programs were written and experimented on real life data set and results obtained have been reported. The correctness of the algorithm has also been proved.
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