Rule discovery is one of the central tasks of data mining. Existing research has produced many algorithms for the purpose. These algorithms, however, often generate too many rules. In the past few years, rule interestingness techniques were proposed to help the user find interesting rules. These techniques typically employ the dataset as a whole to mine rules, and then filter and/or rank the discovered rules in various ways. We argue that this is insufficient. These techniques are unable to answer a question that is of critical importance to the application of rules, i.e., can the rules be trusted? In practice, the users are always concerned with the question. They want to know whether the rules indeed represent some true and stable (or reliable) underlying relationships in the domain. If a rule is not stable, does it show any systematic pattern such as a trend? Before any rule can be used, these questions must be answered. The paper proposes a technique to use statistical methods to analyze rules from the temporal dimension to answer these questions. Experimental results show that the proposed technique is very effective.

Building predictive models and finding useful rules are two important tasks of data mining. While building predictive models has been well studied, finding useful rules for action still presents a major problem. A main obstacle is that many data mining algorithms often produce too many rules. Existing research has shown that most of the discovered rules are actually redundant or insignificant. Pruning techniques have been developed to remove those spurious and/or insignificant rules. In this paper, we argue that being a significant rule (or a non-redundant rule), however, does not mean that it is a potentially useful rule for action. Many significant rules (unpruned rules) are in fact not actionable. This paper studies this issue and presents an efficient algorithm to identify these non-actionable rules. Experiment results on many real-life datasets show that the number of non-actionable rules is typically quite large. The proposed technique thus enables the user to focus on fewer rules and to be assured that the remaining rules are non-redundant and potentially useful for action.

The world around us changes constantly. Knowing what has changed is an important part of our lives. For businesses, recognizing changes is also crucial. It allows businesses to adapt themselves to the changing market needs. In this paper, we study changes of association rules from one time period to another. One approach is to compare the supports and/or confidences of each rule in the two time periods and report the differences. This technique, however, is too simplistic as it tends to report a huge number of rule changes, and many of them are, in fact, simply the snowball effect of a small subset of fundamental changes. Here, we present a technique to highlight the small subset of fundamental changes. A change is fundamental if it cannot be explained by some other changes. The proposed technique has been applied to a number of real-life datasets. Experiments results show that the number of rules whose changes are unexplainable is quite small (about 20% of the total number of changes discovered), and many of these unexplainable changes reflect some fundamental shifts in the application domain.

Ever since the beginning of the Web, finding useful information from the Web has been an important problem. Existing approaches include keyword-based search, wrapper-based information extraction, Web query and user preferences. These approaches essentially find information that matches the user's explicit specifications. This paper argues that this is insufficient. There is another type of information that is also of great interest, i.e., unexpected information, which is unanticipated by the user. Finding unexpected information is useful in many applications. For example, it is useful for a company to find unexpected information bout its competitors, e.g., unexpected services and products that its competitors offer. With this information, the company can learn from its competitors and/or design counter measures to improve its competitiveness. Since the number of pages of a typical commercial site is very large and there are also many relevant sites (competitors), it is very difficult for a human user to view each page to discover the unexpected information. Automated assistance is needed. In this paper, we propose a number of methods to help the user find various types of unexpected information from his/her competitors' Web sites. Experiment results show that these techniques are very useful in practice and also efficient.

The Web not only contains a huge amount of information, but also provides a powerful infrastructure for communication and information sharing. While mining for valuable information or resources from the Web is an active research area, the authors focus on discussing the use of the Web for data mining applications. In particular, they show that the Web can be used to facilitate delivery and interpretation of a set of discovered rules.

The education domain offers a fertile ground for many interesting and challenging data mining applications.These applications can help both educators and students, and improve the quality of education.In this paper, we present a real-life application for the Gifted Education Programme (GEP) of the Ministry of Education (MOE) in Singapore.The application involves many data mining tasks.This paper focuses only on one task, namely, selecting students for remedial classes.Traditionally, a cut-off mark for each subject is used to select the weak students.That is, those students whose scores in a subject fall below the cut-off mark for the subject are advised to take further classes in the subject.In this paper, we show that this traditional method requires too many students to take part in the remedial classes.This not only increases the teaching load of the teachers, but also gives unnecessary burdens to students, which is particularly undesirable in our case because the GEP students are generally taking more subjects than non-GEP students, and the GEP students are encouraged to have more time to explore advanced topics.With the help of data mining, we are able to select the targeted students much more precisely.

Association rules are a fundamental class of patterns that exist in data. The key strength of association rule mining is its completeness. It finds all associations in the data that satisfy the user specified minimum support and minimum confidence constraints. This strength, however, comes with a major drawback. It often produces a huge number of associations. This is particularly true for data sets whose attributes are highly correlated. The huge number of associations makes it very difficult, if not impossible, for a human user to analyze in order to identify those interesting/useful ones. In this paper, we propose a novel technique to overcome this problem. The technique first prunes the discovered associations to remove those insignificant associations, and then finds a special subset of the unpruned associations to form a summary of the discovered associations. We call this subset of associations the direction setting (DS) rules as they set the directions that are followed by the...

Association rule mining is an important model in data mining. Its mining algorithms discover all item associations (or rules) in the data that satisfy the user-specified minimum support (minsup) and minimum confidence (minconf) constraints. Minsup controls the minimum number of data cases that a rule must cover. Minconf controls the predictive strength of the rule. Since only one minsup is used for the whole database, the model implicitly assumes that all items in the data are of the same nature and/or have similar frequencies in the data. This is, however, seldom the case in reallife applications. In many applications, some items appear very frequently in the data, while others rarely appear. If minsup is set too high, those rules that involve rare items will not be found. To find rules that involve both frequent and rare items, minsup has to be set very low. This may cause combinatorial explosion because those frequent items will be associated with one another in all possible ways. T...

1. Introduction Data mining aims to develop a new generation of tools tointelligently assist humans in analyzing mountains of data.