S. Tsur. Data dredging. IEEE Data Engineering Bulletin, 13(4):58--63, December 1990. 32  Home/Search   Document Not in Database   Summary   Related Articles   Check

....Experiments show that the AprioriHybrid has excellent scale up properties, opening up the feasibility of mining association rules over very large databases. The problem of finding association rules falls within the purview of database mining [AIS93a] ABN92] HS94] MKKR92] S 93] Tsu90] also called knowledge discovery in databases [HCC92] Lub89] PS91b] Related, but not directly applicable, work includes the induction of classification rules [BFOS84] Cat91] FWD93] HCC92] Qui93] discovery of causal rules [CH92] Pea92] learning of logical definitions [MF92] Qui90] ....

....on quantifying the usefulness or interestingness of a rule [PS91a] What is useful or interesting is often application dependent. The need for a human in the loop and providing tools to allowhuman guidance of the rule discovery process has been articulated, for example, in [B 93] KI91] Tsu90] We do not discuss these issues in this paper, except to point out that these are necessary features of a rule discovery system that may use our algorithms as the engine of the discovery process. 1.1 Problem Decomposition and Paper Organization The problem of discovering all association rules ....

S. Tsur. Data dredging. IEEE Data Engineering Bulletin, 13(4):58--63, December 1990. 32

....Experiments show that the AprioriHybrid has excellent scale up properties, opening up the feasibility of mining association rules over very large databases. The problem of finding association rules falls within the purview of database mining [AIS93a] ABN92] HS94] MKKR92] S 93] Tsu90] also called knowledge discovery in databases [HCC92] Lub89] PS91b] Related, but not directly applicable, work includes the induction of classification rules [BFOS84] Cat91] FWD93] HCC92] Qui93] discovery of causal rules [CH92] Pea92] learning of logical definitions [MF92] Qui90] ....

....on quantifying the usefulness or interestingness of a rule [PS91a] What is useful or interesting is often application dependent. The need for a human in the loop and providing tools to allow human guidance of the rule discovery process has been articulated, for example, in [B 93] KI91] Tsu90] We do not discuss these issues in this paper, except to point out that these are necessary features of a rule discovery system that may use our algorithms as the engine of the discovery process. 1.1 Problem Decomposition and Paper Organization The problem of discovering all association rules ....

S. Tsur. Data dredging. IEEE Data Engineering Bulletin, 13(4):58--63, December 1990.

....negatives. This may be useful when such results are sufficient. 2. The algorithm is inherently parallel in nature and can be parallelized with minimal communication and synchronization between the processing nodes. Other related, but not directly applicable work in database mining are reported in [7, 10, 6, 9, 3, 13, 14]. The paper is organized as follows: in the next section, we give a formal description of the problem. In Section 2, we describe the problem and give an overview of the previous algorithms. In section 3, we describe our algorithm. Performance results are described in section 4. An approach to ....

S. Tsur. Data dedging. IEEE Data Engineering Bulletin, 13(4):58--63, December 1990.

....of the queries which result in modified knowledge that will be used in the next cycle and so on, until the satisfactory discovery of results. This idea is depicted in Fig. 1. The model we promote in this paper is the happy marriage of two branches of research: the Data Dredging idea presented in [Tsu90] 2 and the Machine Learning approach presented in [She90, She92] The former offers the later a deductive method to access real databases for verifying hypotheses, while the later offers the former the idea of a discovery loop that involves deduction, induction, and human intuition, and a set of ....

Shalom Tsur. Data dredging. IEEE Data Engineering Bulletin, 13(4):58--63, December 1990.

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