On Interactive Data Mining

On Interactive Data Mining

Yan Zhao (University of Regina, Canada)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-60566-010-3.ch168
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Exploring and extracting knowledge from data is one of the fundamental problems in science. Data mining consists of important tasks, such as description, prediction and explanation of data, and applies computer technologies to nontrivial calculations. Computer systems can maintain precise operations under a heavy information load, and also can maintain steady performance. Without the aid of computer systems, it is very difficult for people to be aware of, to extract, to search and to retrieve knowledge in large and separate datasets, let alone interpreting and evaluating data and information that are constantly changing, and then making recommendations or predictions based on inconsistent and/or incomplete data. On the other hand, the implementations and applications of computer systems reflect the requests of human users, and are affected by human judgement, preference and evaluation. Computer systems rely on human users to set goals, to select alternatives if an original approach fails, to participate in unanticipated emergencies and novel situations, and to develop innovations in order to preserve safety, avoid expensive failure, or increase product quality (Elm, et al., 2004; Hancock & Scallen, 1996; Shneiderman, 1998). Users possess varied skills, intelligence, cognitive styles, and levels of tolerance of frustration. They come to a problem with diverse preferences, requirements and background knowledge. Given a set of data, users will see it from different angles, in different aspects, and with different views. Considering these differences, a universally applicable theory or method to serve the needs of all users does not exist. This motivates and justifies the co-existence of numerous theories and methods of data mining systems, as well as the exploration of new theories and methods. According to the above observations, we believe that interactive systems are required for data mining tasks. Generally, interactive data mining is an integration of human factors and artificial intelligence (Maanen, Lindenberg and Neerincx, 2005); an interactive system is an integration of a human user and a computer machine, communicating and exchanging information and knowledge. Through interaction and communication, computers and users can share the tasks involved in order to achieve a good balance of automation and human control. Computers are used to retrieve and keep track of large volumes of data, and to carry out complex mathematical or logical operations. Users can then avoid routine, tedious and error-prone tasks, concentrate on critical decision making and planning, and cope with unexpected situations (Elm, et al., 2004; Shneiderman, 1998). Moreover, interactive data mining can encourage users’ learning, improve insight and understanding of the problem to be solved, and stimulate users to explore creative possibilities. Users’ feedback can be used to improve the system. The interaction is mutually beneficial, and imposes new coordination demands on both sides.
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The importance of human-machine interaction has been well recognized and studied in many disciplines. One example of interactive systems is an information retrieval system or a search engine. A search engine connects users to Web resources. It navigates searches, stores and indexes resources and responses to users’ particular queries, and ranks and provides the most relevant results to each query. Most of the time, a user initiates the interaction with a query. Frequently, feedback will arouse the user’s particular interest, causing the user to refine the query, and then change or adjust further interaction. Without this mutual connection, it would be hard, if not impossible, for the user to access these resources, no matter how important and how relevant they are. The search engine, as an interactive system, uses the combined power of the user and the resources, to ultimately generate a new kind of power.

Though human-machine interaction has been emphasized for a variety of disciplines, until recently it has not received enough attention in the domain of data mining (Ankerst, 2001; Brachmann & Anand, 1996; Zhao & Yao, 2005). In particular, the human role in the data mining processes has not received its due attention. Here, we identify two general problems in many of the existing data mining systems:

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