Datasets for Itemset, Sequence and Tree Mining

There are three different datasets included, that can be used for itemset, sequence and tree mining methods. <strong>dense_db.zip</strong> contains various real itemset datasets like <strong>chess</strong>, <strong>connect</strong>, <strong>mushroom</strong>, <strong>pumsb, T10I4D100K, T40I10D100K</strong> and so on, used in the papers on frequent, closed and maximal itemset mining. For example, <strong>Mohammed J. Zaki</strong> and Ching-Jui Hsiao. <strong>Efficient algorithms for mining closed itemsets and their lattice structure.</strong> <em>IEEE Transactions on Knowledge and Data Engineering</em>, 17(4):462–478, April 2005. doi:10.1109/69.846291. Or Karam Gouda and <strong>Mohammed J. Zaki</strong>. <strong>Genmax: an efficient algorithm for mining maximal frequent itemsets.</strong> <em>Data Mining and Knowledge Discovery: An International Journal</em>, 11(3):223–242, November 2005. doi:10.1007/s10618-005-0002-x. <strong>plandata.zip</strong>: Planning dataset for sequence mining. It was used in the paper <strong>Mohammed J. Zaki</strong>, Neal Lesh, and Mitsunori Ogihara. <strong>PLANMINE: predicting plan failures using sequence mining.</strong> <em>Artificial Intelligence Review</em>, 14(6):421–446, December 2000. Special issue on Applications of Data Mining. doi:https://doi.org/10.1023/A:1006612804250. <strong>cslogs.zip</strong>: The CSLOGS data was used for tree mining, e.g., in <strong>Mohammed J. Zaki</strong>. <strong>Efficiently mining frequent trees in a forest: algorithms and applications.</strong> <em>IEEE Transactions on Knowledge and Data Engineering</em>, 17(8):1021–1035, August 2005. Special issue on Mining Biological Data. doi:10.1109/TKDE.2005.125.