With the fast increase in Web activities, Web data mining has recently become an important research topic and is receiving a significant amount of interest from both academic and industrial environments. While existing methods are efficient for the mining of frequent path traversal patterns from the access information contained in a log file, these approaches are likely to over evaluate associations. Explicitly, most previous studies of mining path traversal patterns are based on the model of a uniform support threshold, where a single support threshold is used to determine frequent traversal patterns without taking into consideration such important factors as the length of a pattern, the positions of Web pages, and the importance of a particular pattern, etc. As a result, a low support threshold will lead to lots of uninteresting patterns derived whereas a high support threshold may cause some interesting patterns with lower supports to be ignored. In view of this, this paper broadens the horizon of frequent path traversal pattern mining by introducing a flexible model of mining Web traversal patterns with dynamic thresholds. Specifically, we study and apply the Markov chain model to provide the determination of support threshold of Web documents; and further, by properly employing some effective techniques devised for joining reference sequences, the proposed algorithm dynamic threshold miner (DTM) not only possesses the capability of mining with dynamic thresholds, but also significantly improves the execution efficiency as well as contributes to the incremental mining of Web traversal patterns. Performance of algorithm DTM and the extension of existing methods is comparatively analyzed with synthetic and real Web logs. It is shown that the option of algorithm DTM is very advantageous in reducing the number of unnecessary rules produced and leads to prominent performance improvement.

When sequential patterns are generated, the newly arriving patterns may not be identified as frequent sequential patterns due to the existence of old data and sequences. In practice, users are usually more interested in the recent data than the old ones. To capture the dynamic nature of data addition and deletion, we propose a general model of sequential pattern mining with a progressive database. In addition, we present a progressive concept to progressively discover sequential patterns in recent time period of interest.

With the fast increase in Web activities, Web data mining has recently become an important research topic. However, most previous studies of mining path traversal patterns are based on the model of a uniform support threshold without taking into consideration such important factors as the length of a pattern, the positions of Web pages, and the importance of a particular pattern, etc. In view of this, we study and apply the Markov chain model to provide the determination of support threshold of Web documents. Furthermore, by properly employing some techniques devised for joining reference sequences, a new mining procedure of Web traversal patterns is proposed in this paper.

The discovery of association relationship among the data in a huge database has been known to be useful in selective marketing, decision analysis, and business management. A significant amount of research effort has been elaborated upon the development of efficient algorithms for data mining. However, without fully considering the time-variant characteristics of items and transactions, it is noted that some discovered rules may be expired from users' interest. In other words, some discovered knowledge may be obsolete and of little use, especially when we perform the mining schemes on a transaction database of short life cycle products. This aspect is, however, rarely addressed in prior studies. To remedy this, we broaden in this paper the horizon of frequent pattern mining by introducing a weighted model of transaction-weighted association rules in a time-variant database. Specifically, we propose an efficient Progressive Weighted Miner (abbreviatedly as PWM) algorithm to perform the mining for this problem as well as conduct the corresponding performance studies. In algorithm PWM, the importance of each transaction period is first reflected by a proper weight assigned by the user. Then, PWM partitions the time-variant database in light of weighted periods of transactions and performs weighted mining. Algorithm PWM is designed to progressively accumulate the itemset counts based on the intrinsic partitioning characteristics and employ a filtering threshold in each partition to early prune out those cumulatively infrequent 2-itemsets. With this design, algorithm PWM is able to efficiently produce weighted association rules for applications where different time periods are assigned with different weights and lead to results of more interest.

Classification is an important problem in data mining and machine learning, and the decision tree approach has been identified as an efficient means for classification. According to our observation on real data, the distribution of attributes with respect to information gain is very sparse because only a few attributes are major discriminating attributes where a discriminating attribute is an attribute, by whose value we are likely to distinguish one tuple from another. In this paper, we propose an efficient decision tree classifier for categorical attribute of sparse distribution. In essence, the proposed Inference Based Classifier (abbreviated as IBC) can alleviate the ôoverfittingö problem of conventional decision tree classifiers. Also, IBC has the advantage of deciding the splitting number automatically based on the generated partitions. IBC is empirically compared to C4.5, SLIQ and K-means based classifiers. The experimental results show that IBC significantly outperforms the companion methods in execution efficiency for dataset with categorical attributes of sparse distribution while attaining approximately the same classification accuracies. Consequently, IBC is considered as an accurate and efficient classifier for sparse categorical attributes.