Methods to discover laws are reviewed from among both statistical approach and artificial intelligence approach with more emphasis placed on the latter. Dimensions discussed are variable dependency checking, passive or active data gathering, single or multiple laws discovery, static (equilibrium) or dynamic (transient) behavior, quantitative (numeric) or qualitative or structural law discovery, and use of domain-general knowledge. Some of the representative discovery systems are also briefly discussed in conjunction with the methods used in the above dimensions.

This paper proposes two topology discovery algorithms for IP networks, namely, a network layer topology discovery algorithm and a link layer topology discovery algorithm. The network layer topology discovery algorithm discovers the subnets and devices in the network of interest and the connections among them. The devices in a subnet can be found by a network layer topology discovery algorithm; however, the connections among the devices cannot be obtained. The link layer topology discovery algorithm is proposed to find the devices in a subnet and the connections among them. The two algorithm are integrated to find the detailed topology map of an IP network. The proposed topology discovery algorithms are implemented based on the Tcl/Tk and Scotty environment. Some implementation details are discussed.

This paper reviews design aspects of computational discovery systems through the analysis of some successful discovery systems. We first review the concept of viewscope/view on data which provides an interpretation of raw data in a specific domain. Then we relate this concept to the KDD process described by Fayyad et al. (1996) and the developer's role in computational discovery due to Langley (1998). We emphasize that integration of human experts and discovery systems is a crucial problem in designing discovery systems and claim together with the analysis of discovery systems that the concept of viewscope/view gives a way for approaching this problem.

Decision trees are used as a convenient means to explain given positive examples and negative examples, which is a form of data mining and knowledge discovery. Standard methods such as ID3 may provide non-monotonic decision trees in the sense that data with larger values in all attributes are sometimes classified into a class with a smaller output value. (In the case of binary data, this is equivalent to saying that the discriminant Boolean function that the decision tree represents is not positive. ) A motivation of this study comes from an observation that real world data are often positive, and in such cases it is natural to build decision trees which represent positive (i. e. , monotone) discriminant functions. For this, we propose how to modify the existing procedures such as ID3, so that the resulting decision tree represents a positive discriminant function. In this procedure, we add some new data to recover the positivity of data, which the original data had but was lost in the process of decomposing data sets by such methods as ID3. To compare the performance of our method with existing methods, we test (1) positive data, which are randomly generated from a hidden positive Boolean function after adding dummy attributes, and (2) breast cancer data as an example of the real-world data. The experimental results on (1) tell that, although the sizes of positive decision trees are relatively larger than those without positivity assumption, positive decision trees exhibit higher accuracy and tend to choose correct attributes, on which the hidden positive Boolean function is defined. For the breast cancer data set, we also observe a similar tendency; i. e. , positive decision trees are larger but give higher accuracy.

Call tracking data contains a calling address, called address, service type, and other useful attributes to predict a customer's calling activity. Call tracking data is becoming a target of data mining for telecommunication carriers. Conventional data-mining programs control the number of association rules found with two types of thresholds (minimum confidence and minimum support), however, often they generate too many association rules because of the wide variety of patterns found in call tracking data. This paper proposes a new method to reduce the number of generated rules. The method proposed tests each generated rule based on Akaike Information Criteria (AIC) without using conventional thresholds. Experiments with artificial call tracking data show the high performance of the proposed method.

The intelligent tutoring system (ITS) enables students to learn knowledge deductively. However, students often become passive, because the ITS takes the initiative in their learning process. Also their knowledge is often superficial, beacause they can not understand different kinds of knowledge due to their limited experience. This paper presents a virtual learning environment (VLE) for discovery learning. The VLE has been built with virtual reality (VR) technology, and supports the student's discovery learning activity and fosters his/her creativity and adaptability based on a broad range of experience by using the functions of VR such as interactivity, direct manipulation interface, walk-through, the function to change view point freely. Also, the VLE connects the explorative training by means of VR with guided education by the ITS. The student model in VLE evaluates the student's level of understanding and adjusts the training accordingly. We have built an operator training system for the training of control activities of electric power plant using the conception of the VLE. The purposes of this system are the following: to aid students to acquire adequate knowledge and skills, and to aid them to gain confidence and experience through their learning activities. The student model evaluates the student's level of understanding for experiential knowledge connected that of skills in VR with that of knowledge in ITS.