J.S. Park, M. Chen, and P.S. Yu, "An Effective Hash Based Algorithm for Mining Association Rules," Proc. ACM SIGMOD Int'l Conf. Management of Data, May 1995.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....itemsets. Once the frequent itemsets are generated, the second step is to derive rules with at least minimum confidence. Since the second step is straightforward, most algorithms focus on the first step, i.e. the frequent itemset generation. Some important algorithms include Apriori [AS94] DHP [PCY95a], Partition [SON95] DIC [BMU 97] and FP growth [HPY00] The discovery of association rules can help in many business decision making processes, such as cross sell, shelf layout, and catalog design. Classification is another basic data mining technique. In the classification task, each tuple ....

....processing in the initial 18 iterations dominates the total execution cost [AIS94] The initial candidate set generation, especially for the large 2 itemsets, is the key issue to improving the performance. Based on the above concern, the DHP (Direct Hashing and Pruning) algorithm was proposed [PCY95a]. DHP is a hash based algorithm and is especially effective for the generation of candidate 2 itemsets. The DHP algorithm has two major features: efficient generation for large itemsets and effective reduction on transaction database sizes. Instead of including all the k itemsets from L k 1 L k 1 ....

J. S. Park, M. S. Chen, and P. S. Yu, "An Effective Hash-Based Algorithm for Mining Association Rules," Proceedings of the ACM SIGMOD, San Jose, CA, May 1995, pp. 175-186. 132

.... and Yang studied the problem of mining association rules over interval data [MY97] using the Birch clustering algorithm [ZRL96] A lot of attempts have been made to improve or to alter slightly the basic algorithm underlying the approaches discussed above [ASY98, CA97, FMMT96, KMR 94, NLH98, Par95, TUA 98, WYY00] Chapter 7 presents an experimental framework intended to demonstrate the benefits of semantic optimization using association rules. The programs developed to identify these rules are based on standard algorithms for identifying quantitative rules [SA96] and rules over interval ....

....the consequent. Support measures the popularity of a rule, since it reflects the percentage of objects of the extent satisfying both the antecedent and the consequent. A number of algorithms have been proposed for mining association rules over categorical or quantitative data [AIS93, SA96, MY97, Par95, TUA 98] In the remainder of the chapter, we assume that the process of mining rules has been completed and that we already have a warehouse with a set of rules for each extent in the database. Further, in this warehouse we store the exceptions to each rule, i.e. those objects that satisfy ....

J.S. Park. An effective hash-based algorithm for mining association rules. In ACM SIGMOD Intl Conf on Management of Data, pages 175--186, 1995.

....This is where knowledge discovery in databases (KDD) comes into play. Today, among the most popular techniques in KDD, is the extraction of association rules from large databases. While many researchers have significantly contributed to the development of efficient association rule algorithms [1 3, 10, 21, 26], literature on the use of this technique in concrete real world applications remains rather limited [4, 5, 25] Nevertheless, the widespread acceptance of association rules as a valuable technique to solve real business problems will largely depend on the successful application of this technique ....

Park, J., Chen, M., and Yu, Ph. An effective hash based algorithm for mining association rules. In Carey, M., Schneider, D. (eds.). Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, 175-186.

....those based on the level wise paradigm; nevertheless, they still require a substantial amount of time. A number of efficient and scalable parallel formulations have been developed for finding frequent itemsets and sequences that are based on the candidate generation and counting framework [3, 18, 22, 16, 4], both for shared and distributed memory parallel computers [2, 22, 17, 8, 25, 29, 20] However, the problem of parallelizing equivalence class based and projection based algorithms has received relatively little attention and existing parallel formulations for them have been targeted only toward ....

....generated (l 1) length frequent itemsets. The GSP [27] algorithm extended the Apriori like level wise mining method to find frequent patterns in sequential datasets. The basic level wise algorithm has been extended in a number of different ways leading to more efficient algorithms such as DHP [19, 18], Partition [22] SEAR and Spear [16] and DIC [4] An entirely different approach for finding frequent itemsets and sequences are the equivalence class based algorithms Eclat [32] and SPADE [31] that break the large search space of frequent patterns into small and independent chunks and use a ....

J.S. Park, M.S. Chen, and P.S. Yu. An effective hash-based algorithm for mining association rules. In Proc. of 1995.

....generated (l D length frequent itemsets. The GSP [20] algorithm extended the Apriori like level wise mining method to find frequent patterns in sequential databases. The basic level wise algorithm has been extended in a number of different ways leading to more efficient algorithms such as DHP [14, 13], Partition [19] SEAR and Spear [12] and DIC [5] An entirely different approach for finding frequent itemsets and sequences are the equivalence class based algorithms Eclat [26] and SPADE [24] that break the large search space of frequent patterns into small and independent chunks and use a ....

J.S. Park, M.S. Chen, and P.S. Yu. An effective hash-based algorithm for mining association rules. In Proc. of 1995.

....those based on the level wise paradigm; nevertheless, they still require a substantial amount of time. A number of efficient and scalable parallel formulations have been developed for finding frequent itemsets and sequences that are based on the candidate generation and counting framework [3, 18, 22, 16, 4], both for shared and distributed memory parallel computers [2, 22, 17, 8, 25, 29, 20] However, the problem of parallelizing equivalence class based and projection based algorithms has received relatively litfie attention and existing parallel formulations for them have been targeted only toward ....

....generated (l 1) length frequent itemsets. The GSP [27] algorithm extended the Apriori like level wise mining method to find frequent patterns in sequential datasets. The basic level wise algorithm has been extended in a number of different ways leading to more efficient algorithms such as DHP [19, 18], Partition [22] SEAR and Spear [16] and DIC [4] An entirely different approach for finding frequent itemsets and sequences are the equivalence class based algorithms Eclat [32] and SPADE [31] that break the large search space of frequent patterns into small and independent chunks and use a ....

J.S. Park, M.S. Chen, and P.S. Yu. An effective hash-based algorithm for mining association rules. In Proc. of 1995.

....T k , the generators field gives the IDs of the two itemsets that generated c k . If these itemsets are present in the entry for t.set of itemsets, c k is present in transaction t.TID. Hence add c k to C t . 3. 4 The DHP Algorithm Direct Hashing with Efficient pruning for Fast Data Mining (DHP) [82] is a hash based algorithm for mining association rules. Here, the size of the database is reduced in the successive scans of the database. Unlike in the case of APRIORITID, in DHP transactions are deleted, as well as some members of the transactions are removed from the database for later passes. ....

....may not be substantial since the number of large 2 itemsets (in the increment) would typically be negligible compared to the total number of candidate 2 itemsets. Moreover, if the number of known potentially large 2 itemsets is small, the AprioriGen function is known to be efficient in its pruning [82]. Hence the computed negative border closure after the second pass would not be much larger than what would be obtained by performing multiple passes. Therefore, DELTA does not follow this approach and chooses to perform only one additional pass over the increment. Finally, DELTA performs a third ....

J. Park, M. Chen and P. Yu, "An effective hash-based algorithm for mining association rules", Proc. of 24th SIGMOD Conf., May 1995.

....denote MFI as the set of all maximal frequent itemsets. Any maximal frequent itemset X is a frequent closed itemset since no nontrivial superset of X is frequent. Thus we have FI FCI MFI . There are three different approaches for generating FI. First, candidate set generate and test approach [1,11,14,8,12,7]: most previous algorithms belong to this group. The basic idea is to generate and then test the candidate set. This process is repeated in a bottom up fashion until no candidate set can be formed. Second, sampling approach [7] it selects samples of a dataset to form the candidate set. The ....

J. S. Park, M. Chen, and P. S. Yu. An effective hash based algorithm for mining association rules. In Proc. ACM SIGMOD Intl. Conf. Management of Data, May 1995.

....rules [1] the develop ment of effective mechanisms for mining large databases has been the subject of numerous studies, which can be broadly divided into two groups. The first group includes studies focusing on performance and efficiency issues, e.g. the Apriori framework [2, 11] partitioning [16], sampling [24] incremental updating [6] etc. The second group includes studies that go beyond the initial notion of association rules to other kinds of mined rules, e.g. multi level rules [8, 21] quantitative and multi dimensional rules [22, 7, 14, 10] rules with item constraints [23] ....

J.S. Park, M.S. Chen, and P.S. Yu. An effective hash-based algorithm for mining association rules. In Proc. 1995 ACMSIGMOD, pp 175-186.

....from the base transaction. This is quite different from the classical definition of itemset (i,i, ik) in which all items lie within the same transactions. To find the frequent item sets, two algorithms, EApriori and EH Apriori, were implemented which are extensions of Apriori based algorithms [2, 8]. Let L represent the set of frequent k itemsets, and C the set of candidate k itemsets. Both algorithms make multiple passes over the database. Each pass consists of two phases. First, the set of all frequent (k 1) itemsets L , found in the (k 1)th pass, is used to generate the candidate itemset ....

....each list of consecutive transactions, they determine which candidates in C are contained and increment their counts. At the end of the pass, C is examined to check which of the candidates are actually frequent, yielding L. The algorithms terminate when L becomes empty. As previously reported in [8], the processing cost of the first two iterations (i.e. obtaining L and L) dominates the total mining cost. The reason is that, for a given minimum support, we usually have a very large L, which in turn results in a huge number of itemsets in C to process. In the inter transaction association ....

[Article contains additional citation context not shown here]

J.-S. Park, M.-S. Chen, and P.S. Yu. An effective hash based algorithm for mining association rules. In Proc. of the ACM $IGMOD Conference on Management of Data, pages 175-186, San Jose, CA, May 1995.

....generated (l 1) length frequent itemsets. The GSP [20] algorithm extended the Apriori like level wise mining method to find frequent patterns in sequential databases. The basic level wise algorithm has been extended in a number of different ways leading to more efficient algorithms such as DHP [14, 13], Partition [19] SEAR and Spear [12] and DIC [5] An entirely different approach for finding frequent itemsets and sequences are the equivalence class based algorithms Eclat [26] and SPADE [24] that break the large search space of frequent patterns into small and independent chunks and use a ....

J.S. Park, M.S. Chen, and P.S. Yu. An effective hash-based algorithm for mining association rules. In Proc. of 1995.

....I Introduction Since its introduction [1] the problem of mining association rules from large databases has been the subject of numerous studies. These studies cover a broad spectrum of topics including: i) fast algorithms based on the levelwise Apriori framework [3, 13] partitioning [19, 18], and sampling [24] ii) incremental updating and parallel algorithms [6, 2, 8] iii) mining of generalized and multi level rules [21, 9] iv) mining of quantitative rules [22, 16] v) mining of multidimensional rules [7, 14, 12] vi) mining rules with item constraints [23] and (vii) ....

....wants to focus the generation of rules to a specific, small subset of candidates, based on properties of the data Such a black box model would be tolerable if the turnaround time of the computation were small, e.g. a few seconds. However, despite the development of many efficient algorithms [2, 3, 6, 8, 13, 18, 19, 24], association mining remains a process typically taking hours to complete. Before a new invocation of the black box, the user is not allowed to preempt the process and needs to wait for hours. Furthermore, typically only a small fraction of the computed rules might be what the user was looking ....

J.S. Park, M.S. Chen, and P.S. Yu. An effective hash-based algorithm for mining association rules. SIGMOD 95, pp 175-186.

No context found.

J.S. Park, M. Chen, and P.S. Yu, "An Effective Hash Based Algorithm for Mining Association Rules," Proc. ACM SIGMOD Int'l Conf. Management of Data, May 1995.

No context found.

J. S. Park, M.-S. Chen, and P. S. Yu. An Effective Hash Based Algorithm for Mining Association Rules. In Proc. of the 1995.

No context found.

Park J.S., Chen M.-S., Yu P. S.: An Effective Hash-Based Algorithm for Mining Association Rules. Proc. 1995 ACM SIGMOD International Conference on Management of Data, San Jose, CA, USA (1995)

No context found.

J. S. Park, M. Chen, and P. S. Yu. An effective hash based algorithm for mining association rules. In ACM SIGMOD Intl. Conf. Management of Data, May 1995.

No context found.

J. S. Park, M.-S. Chen, and P. S. Yu. An effective hash based algorithm for mining association rules. In M. J. Carey and D. A. Schneider, editors, Proceedings of the 1995.

No context found.

J. S. Park, M.-S. Chen, and P. S. Yu. An Effective Hash Based Algorithm for Mining Association Rules. In Proc. of the 1995.

No context found.

J. S. Park, M.-S. Chen, and P. S. Yu. An effective hash based algorithm for mining association rules. In SIGMOD95, pages 175--186, 1995.

No context found.

J. S. Park, M.-S. Chen, and P. S. Yu, "An effective HashBased Algorithm for Mining Association Rules", Proceedings of the ACM SIGMOD, San Jose, CA, May 1995, pp. 175-186.

No context found.

J. S. Park, M.-S. Chen, and P. S. Yu. An effective hashbased algorithm for mining association rules. In Proc. of pages 175 -- 186, San Jose, California, May 1995.

No context found.

Park J.S., Chen M.-S., Yu P. S., "An Effective Hash-Based Algorithm for Mining Association Rules", SIGMOD'95, San Jose, CA, USA, 1995

No context found.

J. S. PARK, M. S. CHEN, AND P. S. YU. An effective hash based algorithm for mining association rules. In Proc. of 1995 ACM SIGMOD, pages 175--186, 1995.

No context found.

J. S. Park, M.-S. Chen, and P. S. Yu. An Effective Hash Based Algorithm for Mining Association Rules. In Proc. of the 1995.

No context found.

J. S. Park, M. Chen, and P. S. Yu. An effective hash based algorithm for mining association rules. In ACM SIGMOD Intl. Conf. Management of Data, May 1995.

First 50 documents  Next 50

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC