Knowledge extraction

About: Knowledge extraction is a research topic. Over the lifetime, 20251 publications have been published within this topic receiving 413401 citations.

Data‐based acquisition and incremental modification of classification rules

Abstract: One of the most important problems in the application of knowledge discovery systems is the identification and subsequent updating of rules. Many applications require that the classification rules be derived from data representing exemplar occurrences of data patterns belonging to different classes. The problem of identifying such rules in data has been researched within the field of machine learning, and more recently in the context of rough set theory and knowledge discovery in databases. In this paper we present an incremental methodology for finding all maximally generalized rules and for adaptive modification of them when new data become available. The methodology is developed in the context of rough set theory and is based on the earlier idea of discernibility matrix introduced by Skowron.

Finding interesting patterns using user expectations

Abstract: One of the major problems in the field of knowledge discovery (or data mining) is the interestingness problem. Past research and applications have found that, in practice, it is all too easy to discover a huge number of patterns in a database. Most of these patterns are actually useless or uninteresting to the user. But due to the huge number of patterns, it is difficult for the user to comprehend them and to identify those interesting to him/her. To prevent the user from being overwhelmed by the large number of patterns, techniques are needed to rank them according to their interestingness. In this paper, we propose such a technique, called the user-expectation method. In this technique, the user is first asked to provide his/her expected patterns according to his/her past knowledge or intuitive feelings. Given these expectations, the system uses a fuzzy matching technique to match the discovered patterns against the user's expectations, and then rank the discovered patterns according to the matching results. A variety of rankings can be performed for different purposes, such as to confirm the user's knowledge and to identify unexpected patterns, which are by definition interesting. The proposed technique is general and interactive.

Uncertain data mining: an example in clustering location data

Abstract: Data uncertainty is an inherent property in various applications due to reasons such as outdated sources or imprecise measurement. When data mining techniques are applied to these data, their uncertainty has to be considered to obtain high quality results. We present UK-means clustering, an algorithm that enhances the K-means algorithm to handle data uncertainty. We apply UK-means to the particular pattern of moving-object uncertainty. Experimental results show that by considering uncertainty, a clustering algorithm can produce more accurate results.