Ching-Yao Wang

Flexible online association rule mining based on multidimensional pattern relations

Most incremental mining and online mining algorithms concentrate on finding association rules or patterns consistent with entire current sets of data. Users cannot easily obtain results from only interesting portion of data. This may prevent the usage of mining from online decision support for multidimensional data. To provide ad-hoc, query-driven, and online mining support, we first propose a relation called the multidimensional pattern relation to structurally and systematically store context and mining information for later analysis. Each tuple in the relation comes from an inserted dataset in the database. We then develop an online mining approach called three-phase online association rule mining (TOARM) based on this proposed multidimensional pattern relation to support online generation of association rules under multidimensional considerations. The TOARM approach consists of three phases during which final sets of patterns satisfying various mining requests are found. It first selects and integrates related mining information in the multidimensional pattern relation, and then if necessary, re-processes itemsets without sufficient information against the underlying datasets. Some implementation considerations for the algorithm are also stated in detail. Experiments on homogeneous and heterogeneous datasets were made and the results show the effectiveness of the proposed approach.

An incremental mining algorithm for maintaining sequential patterns using pre-large sequences

Highlights? In this paper, we propose an incremental mining algorithm for maintaining sequential patterns based on the concept of pre-large sequences to reduce the need for rescanning original databases. ? The proposed algorithm does not require rescanning original databases until the accumulative amount of newly added customer sequences exceeds a safety bound, which depends on database size. ? The proposed approach becomes increasingly efficient as databases grow. Mining useful information and helpful knowledge from large databases has evolved into an important research area in recent years. Among the classes of knowledge derived, finding sequential patterns in temporal transaction databases is very important since it can help model customer behavior. In the past, researchers usually assumed databases were static to simplify data-mining problems. In real-world applications, new transactions may be added into databases frequently. Designing an efficient and effective mining algorithm that can maintain sequential patterns as a database grows is thus important. In this paper, we propose a novel incremental mining algorithm for maintaining sequential patterns based on the concept of pre-large sequences to reduce the need for rescanning original databases. Pre-large sequences are defined by a lower support threshold and an upper support threshold that act as gaps to avoid the movements of sequences directly from large to small and vice versa. The proposed algorithm does not require rescanning original databases until the accumulative amount of newly added customer sequences exceeds a safety bound, which depends on database size. Thus, as databases grow larger, the numbers of new transactions allowed before database rescanning is required also grow. The proposed approach thus becomes increasingly efficient as databases grow.

An efficient and effective association-rule maintenance algorithm for record modification

Modification of records in databases is common in real-world applications. Developing an efficient and effective mining algorithm to maintain discovered information as the records in a database are updated is thus quite important in the field of data mining. Although association rules for modification of records can be maintained by using deletion and insertion procedures, this requires twice the computation time needed for a single procedure. In this paper, we present a new modification algorithm to resolve this issue. The concept of pre-large itemsets is used to reduce the need for rescanning original databases and to save maintenance costs. The proposed algorithm does not require rescanning of original databases until a specified number of records have been modified. If the database is large, then the number of modified records allowed will also be large. This characteristic is especially useful for real-world applications.

Maintenance of sequential patterns for record deletion

We previously proposed an incremental mining algorithm for maintenance of sequential patterns based on the concept of pre-large sequences as new records were inserted. In this paper we attempt to apply the concept of pre-large sequences to maintain sequential patterns as records are deleted. Pre-large sequences are defined by a lower support threshold and an upper support threshold. They act as buffers to avoid the movements of sequential patterns directly from large to small and, vice-versa. Our proposed algorithm does not require rescanning original databases until the accumulative amount of deleted customer sequences exceeds a safety bound, which depends on database size. As databases grow larger, the number of deleted customer sequences allowed before database rescanning is required also grows. The proposed approach is thus efficient for a large database.

PROVIDING TIMELY UPDATED SEQUENTIAL PATTERNS IN DECISION MAKING

Mining knowledge from large databases has become a critical task for organizations. Managers commonly use the obtained sequential patterns to make decisions. In the past, databases were usually assumed to be static. In real-world applications, however, transactions may be updated. In this paper, a maintenance algorithm for rapidly updating sequential patterns for real-time decision making is proposed. The proposed algorithm utilizes previously discovered large sequences in the maintenance process, thus greatly reducing the number of database rescans and improving performance. Experimental results verify the performance of the proposed approach. The proposed algorithm provides real-time knowledge that can be used for decision making.

Maintenance of sequential patterns for record modification using pre-large sequences

In previous work we proposed incremental mining algorithms for maintenance of sequential patterns based on the concept of pre-large sequences as records were inserted or deleted. Although maintenance of sequential patterns for record modification can be performed by using the deletion procedure and then the insertion procedure, double the computation time of a single procedure is needed. In this paper, we attempt to apply the concept of pre-large sequences to maintain sequential patterns as records are modified. The proposed algorithm does not require rescanning original databases until the accumulative number of modified customer sequences exceeds a safety bound derived by a pre-large concept. As databases grow larger, the number of modified customer sequences allowed before database rescanning also needs to grow.

Improved negative-border online mining approaches

In the past, we proposed an extended multidimensional pattern relation (EMPR) to structurally and systematically store previously mining information for each inserted block of data, and designed a negative-border online mining (NOM) approach to provide ad-hoc, query-driven and online mining supports. In this paper, we try to use appropriate data structures and design efficient algorithms to improve the performance of the NOM approach. The lattice data structure is utilized to organize and maintain all candidate itemsets such that the candidate itemsets with the same proper subsets can be considered at the same time. The derived lattice-based NOM (LNOM) approach will require only one scan of the itemsets stored in EMPR, thus saving much computation time. In addition, a hashing technique is used to further improve the performance of the NOM approach since many itemsets stored in EMPR may be useless for calculating the counts of candidates. At last, experimental results show the effect of the improved NOM approaches.