Data Mining: How Research Meets Practical Development?

At the 2001 IEEE International Conference on Data Mining in San Jose, California, on November 29 to December 2, 2001, there was a panel discussion on how data mining research meets practical development. One of the motivations for organizing the panel discussion was to provide useful advice for industrial people to explore their directions in data mining development. Based on the panel discussion, this paper presents the views and arguments from the panel members, the Conference Chair and the Program Committee Co-Chairs. These people as a group have both academic and industrial experiences in different data mining related areas such as databases, machine learning, and neural networks. We will answer questions such as (1) how far data mining is from practical development, (2) how data mining research differs from practical development, and (3) what are the most promising areas in data mining for practical development.     

A knowledge-based system for generating informative responses to indirect database queries

The objective of this study is to develop a knowledge-base framework for generatingcooperative answers to indirect queries. Anindirect query can be considered as a nonstandard database query in which a user did not specify explicitly the information request. In a cooperative query answering system, a user's indirect query should be answered with an informative response, either anaffirmative response or anegative response, which is generated on the basis of the inference of the user's information request and the reformulation of the users' indirect query.This paper presents methods for inferring users' intended actions, determining users' information requirements, and for automatically reformulating indirect queries into direct queries. The inference process is carried out on the basis of a user model, calluser action model, as well as the query context. Two kinds ofinformative responses, i.e.affirmative responses andnegative responses can be generated by arule-based approach.     

FAULT-TOLERANT MULTI-AGENT EXACT BELIEF PROPAGATION

Multiply sectioned Bayesian networks (MSBNs) support multi-agent probabilistic inference in distributed large problem domains, where agents (subdomains) are organized by a tree structure (called hypertree). In earlier work, all belief updating methods on a hypertree are made of two rounds of propagation, each of which is implemented as a recursive process. Both processes need to be started from the same designated (root) hypernode. Agents perform local belief updating at most in a partial parallel manner. Such methods may not be suitable for practical multi-agent environments because they are easy to crush for the problems happened in communication or local belief updating. In this paper, we present a fault-tolerant belief updating method for multi-agent probabilistic inference. In this method, multiple agents concurrently perform exact belief updating in a complete parallel. Temporary problems happened from time to time at some agents or some communication channels would not prevent agents from eventually converging to the correct beliefs. Permanently disconnected communication channels would not keep the properly connected portions of the system from appropriately finishing their belief updating within portions. Compared to the previous traversal-based belief updating, the proposed approach is not only fault-tolerant but also robust and scalable.     

Intelligent Thai text – Thai sign translation for language learning

We present the Intelligent Thai text – Thai sign translation for language learning (IT³STL). IT³STL is able to translate Thai text into Thai sign language simply and conveniently anytime, anywhere. Thai sign language is the language of the deaf in Thailand. In the translation process, the distinction between Thai text and Thai sign language in both grammar and vocabulary are concerned in each processing step to ensure the accuracy of translation. IT³STL was designed not only to be an automatic interpreter but also to be a language tutor assistant. It provides meaning of each word and describes the structure formation and word order of the translated sentence. With IT³STL, the deaf and hearing-impaired are able to enhance their communication ability and to improve their knowledge and learning skills. Moreover IT³STL has increased motivation and opportunity for them to access multimedia and e-learning.     

LEARNING IN RELATIONAL DATABASES: A ROUGH SET APPROACH

Knowledge discovery in databases, or dala mining, is an important direction in the development of data and knowledge-based systems. Because of the huge amount of data stored in large numbers of existing databases, and because the amount of data generated in electronic forms is growing rapidly, it is necessary to develop efficient methods to extract knowledge from databases. An attribute-oriented rough set approach has been developed for knowledge discovery in databases. The method integrates machine-learning paradigm, especially learning-from-examples techniques, with rough set techniques. An attribute-oriented concept tree ascension technique is first applied in generalization, which substantially reduces the computational complexity of database learning processes. Then the cause-effect relationship among the attributes in the database is analyzed using rough set techniques, and the unimportant or irrelevant attributes are eliminated. Thus concise and strong rules with little or no redundant information can be learned efficiently. Our study shows that attribute-oriented induction combined with rough set theory provide an efficient and effective mechanism for knowledge discovery in database systems.     

Discovering Maximal Generalized Decision Rules Through Horizontal and Vertical Data Reduction

We present a method to learn maximal generalized decision rules from databases by integrating discretization, generalization and rough set feature selection. Our method reduces the data horizontally and vertically. In the first phase, discretization and generalization are integrated and the numeric attributes are discretized into a few intervals. The primitive values of symbolic attributes are replaced by high level concepts and some obvious superfluous or irrelevant symbolic attributes are also eliminated. Horizontal reduction is accomplished by merging identical tuples after the substitution of an attribute value by its higher level value in a pre-defined concept hierarchy for symbolic attributes, or the discretization of continuous (or numeric) attributes. This phase greatly decreases the number of tuples in the database. In the second phase, a novel context-sensitive feature merit measure is used to rank the features, a subset of relevant attributes is chosen based on rough set theory and the merit values of the features. A reduced table is obtained by removing those attributes which are not in the relevant attributes subset and the data set is further reduced vertically without destroying the interdependence relationships between classes and the attributes. Then rough set-based value reduction is further performed on the reduced table and all redundant condition values are dropped. Finally, tuples in the reduced table are transformed into a set of maximal generalized decision rules. The experimental results on UCI data sets and a real market database demonstrate that our method can dramatically reduce the feature space and improve learning accuracy.     

Intelligent query answering by knowledge discovery techniques

Knowledge discovery facilitates querying database knowledge and intelligent query answering in database systems. We investigate the application of discovered knowledge, concept hierarchies, and knowledge discovery tools for intelligent query answering in database systems. A knowledge-rich data model is constructed to incorporate discovered knowledge and knowledge discovery tools. Queries are classified into data queries and knowledge queries. Both types of queries can be answered directly by simple retrieval or intelligently by analyzing the intent of query and providing generalized, neighborhood or associated information using stored or discovered knowledge. Techniques have been developed for intelligent query answering using discovered knowledge and/or knowledge discovery tools, which includes generalization, data summarization, concept clustering, rule discovery, query rewriting, deduction, lazy evaluation, application of multiple-layered databases, etc. Our study shows that knowledge discovery substantially broadens the spectrum of intelligent query answering and may have deep implications on query answering in data- and knowledge-base systems