Ming-Cheng Tseng

Mining Generalized Association Rules with Multiple Minimum Supports

Mining generalized association rules in the presence of the taxonomy has been recognized as an important model in data mining. Earlier work on generalized association rules confined the minimum support to be uniformly specified for all items or for items within the same taxonomy level. In this paper, we extended the scope of mining generalized association rules in the presence of taxonomy to allow any form of user-specified multiple minimum supports. We discussed the problems of using classic Apriori itemset generation and presented two algorithms, MMS_Cumulate and MMS_Stratify, for discovering the generalized frequent itemsets. Empirical evaluation showed that these two algorithms are very effective and have good linear scale-up characteristic.

Automated support specification for efficient mining of interesting association rules

In recent years, the weakness of the canonical support-confidence framework for associations mining has been widely studied. One of the difficulties in applying association rules mining is the setting of support constraints. A high-support constraint avoids the combinatorial explosion in discovering frequent itemsets, but at the expense of missing interesting patterns of low support. Instead of seeking a way to set the appropriate support constraints, all current approaches leave the users in charge of the support setting, which, however, puts the users in a dilemma. This paper is an effort to answer this long-standing open question. According to the notion of confidence and lift measures, we propose an automatic support specification for efficiently mining high-confidence and positive-lift associations without consulting the users. Experimental results show that the proposed method is not only good at discovering high-confidence and positive-lift associations, but also effective in reducing spurious frequent itemsets.

A Confidence-Lift Support Specification for Interesting Associations Mining

Recently, the weakness of the canonical support-confidence framework for associations mining has been widely studied in the literature. One of the difficulties in applying association rules mining to real world applications is the setting of support constraint. A high support constraint avoids the combinatorial explosion in discovering frequent itemsets, but at the expense of missing interesting patterns of low support. Instead of seeking the way for setting the appropriate support constraint, all current approaches leave the users in charge of the support setting, which, however, puts the users in a dilemma. This paper is an effort to answer this long-standing open question. Based on the notion of confidence and lift measures, we propose an automatic support specification for mining high confidence and positive lift associations without consulting the users. Experimental results show that this specification is good at discovering the low support, but high confidence and positive lift associations, and is effective in reducing the spurious frequent itemsets.

Incremental maintenance of generalized association rules under taxonomy evolution

Mining association rules from large databases of business data is an important topic in data mining. In many applications, there are explicit or implicit taxonomies (hierarchies) for items, so it may be useful to find associations at levels of the taxonomy other than the primitive concept level. Previous work on the mining of generalized association rules, however, assumed that the taxonomy of items remained unchanged, disregarding the fact that the taxonomy might be updated as new transactions are added to the database over time. If this happens, effectively updating the generalized association rules to reflect the database change and related taxonomy evolution is a crucial task. In this paper, we examine this problem and propose two novel algorithms, called IDTE and IDTE2, which can incrementally update the generalized association rules when the taxonomy of items evolves as a result of new transactions. Empirical evaluations show that our algorithms can maintain their performance even for large numbers of incremental transactions and high degrees of taxonomy evolution, and are faster than applying contemporary generalized association mining algorithms to the whole updated database.

Mining Association Rules with Ontological Information

The problem of mining association rules incorporated with domain knowledge has been studied recently. Previous work was conducted individually on two types of knowledge, classification and composition. In this paper, we revisit this problem from a more unified viewpoint. We consider the problem of mining association rules with ontological information that presents not only classification but also composition relationship. Two effective algorithms are proposed with empirical evaluation displayed.

OLAM Cube Selection in On-Line Multidimensional Association Rules Mining System

Mining association rules from large database is a computation intensive task. To reduce the complexity of association discovery, Lin et al. pro-posed the concept of OLAM (On-Line Association Mining) cube, an extension of Ice-berg cube used to store frequent multidimensional itemsets. They also presented a framework of on-line multidimensional association rule mining system, called OMARS, which relies heavily on the OLAM cubes to provide an OLAP-like association mining environment. This paper is a companion toward the implementation of OMARS. Particularly, we investigate the problem of selecting appropriate OLAM cubes to materialize and store in OMARS. Several properties of the OLAM cube that are useful to the cube selection are presented. We also discuss how to adopt the greedy method to solve the problem under the storage constraint.

Efficient remining of generalized association rules under multiple minimum support refinement

Mining generalized association rules among items in the presence of taxonomy and with nonuniform minimum support has been recognized as an important model in the data mining community. In real applications, however, the work of discovering interesting association rules is an iterative process; the analysts have to continuously adjust the constraint of minimum support to discover real informative rules. How to reduce the response time for each remining process thus becomes a crucial issue. In this paper, we examine the problem of maintaining the discovered multi-supported generalized association rules when the multiple minimum support constraint is refined and propose a novel algorithm called RGA_MSR to accomplish the work. By keeping and utilizing the set of frequent itemsets and negative border, and adopting vertical intersection counting strategy, the proposed RGA_MSR algorithm can significantly reduce the computation time spent on rediscovery of frequent itemsets and has very good performance.

Dynamic Mining of Multi-supported Association Rules with Classification Ontology

One of the predominant techniques used in the area of data mining is association rule mining. In real world, data mining analysts usually are confronted with a dynamic environment; the database would be changed over time, and the analysts may need to set different support constraints to discover real informative rules. Efficiently updating the discovered association rules thus becomes a crucial issue. In this paper, we consider the problem of dynamic mining of association rules with classification ontology and with non-uniform multiple minimum supports constraint. We investigate how to efficiently update the discovered association rules when there is transaction update to the database and the analyst has refined the support constraint. A novel algorithm called DMA_CO is proposed. Experimental results show that our algorithm is 14% to 80% faster than applying generalized associations mining algorithms to the whole updated database.

Maintenance of generalized association rules under transaction update and taxonomy evolution

Mining generalized association rules among items in the presence of taxonomies has been recognized as an important model in data mining. Earlier work on mining generalized association rules ignore the fact that the taxonomies of items cannot be kept static while new transactions are continuously added into the original database. How to effectively update the discovered generalized association rules to reflect the database change with taxonomy evolution and transaction update is a crucial task. In this paper, we examine this problem and propose a novel algorithm, called IDTE, which can incrementally update the discovered generalized association rules when the taxonomy of items is evolved with new transactions insertion to the database. Empirical evaluations show that our algorithm can maintain its performance even in large amounts of incremental transactions and high degree of taxonomy evolution, and is more than an order of magnitude faster than applying the best generalized associations mining algorithms to the whole updated database.

Incremental Mining of Ontological Association Rules in Evolving Environments

The process of knowledge discovery from databases is a knowledge intensive, highly user-oriented practice, thus has recently heralded the development of ontology-incorporated data mining techniques. In our previous work, we have considered the problem of mining association rules with ontological information (called ontological association rules) and devised two efficient algorithms, called AROC and AROS, for discovering ontological associations that exploit not only classification but also composition relationship between items. The real world, however, is not static. Data mining practitioners usually are confronted with a dynamic environment. New transactions are continually added into the database over time, and the ontology of items is evolved accordingly. Furthermore, the work of discovering interesting association rules is an iterative process; the analysts need to repeatedly adjust the constraint of minimum support and/or minimum confidence to discover real informative rules. Under these circumstances, how to dynamically discover association rules efficiently is a crucial issue. In this regard, we proposed a unified algorithm, called MIFO, which can handle the maintenance of discovered frequent patterns taking account of all evolving factors: new transactions updating in databases, ontology evolution and minimum support refinement. Empirical evaluation showed that MIFO is significantly faster than running our previous algorithms AROC and AROS from scratch.