Abstract
Some data mining tasks can produce such great amounts of data that we have to cope with a new knowledge management problem. Frequent itemset mining fits in this category. Different approaches were proposed to handle or avoid somehow this problem. All of them have problems and limitations. In particular, most of them need the original data during the analysis phase, which is not feasible for data streams. The DWFIST (Data Warehouse of Frequent ItemSets Tactics) approach aims at providing a powerful environment for the analysis of itemsets and derived patterns, such as association rules, without accessing the original data during the analysis phase. This approach is based on a Data Warehouse of Frequent Itemsets. It provides frequent itemsets in a flexible and efficient way as well as a standardized logical view upon which analytical tools can be developed. This paper presents how such a data warehouse can be built.
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Monteiro, R.S., Zimbrão, G., Schwarz, H., Mitschang, B., de Souza, J.M. (2005). Building the Data Warehouse of Frequent Itemsets in the DWFIST Approach. In: Hacid, MS., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds) Foundations of Intelligent Systems. ISMIS 2005. Lecture Notes in Computer Science(), vol 3488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11425274_31
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DOI: https://doi.org/10.1007/11425274_31
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