On Interactive Data Mining

Abstract

INTRODUCTIONExploring 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.

S3Mining: A model-driven engineering approach for supporting novice data miners in selecting suitable classifiers

“…User-friendly data mining has emerged as a challenge in recent bibliography related to support novice data miners. Some user-friendly approaches are focused on providing interactive systems, an adaptive and effective communication between human users and computer systems [28] where the user is guided through the data mining process. FIU-Miner [29], is an integrated system that facilitates users to conduct ad-hoc data mining tasks.…”

User-Centered Interactive Data Mining

“…The three-layered conceptual framework represents the understanding, discovery, and utilization of knowledge, and can be easily extended to a user-centered conceptual framework for interactive data mining (Zhao, 2007). The user-centered three-layered framework consists of the philosophy layer, the technique layer, and the application layer, and considers one important factor, user requirements and preferences.…”

“…According to the above observations, we believe that interactive systems are required for data mining tasks. Though human-machine interaction has been emphasized for many disciplines, it did not receive enough attention in the domain of data mining until recently (Brachmann & Anand, 1996;Han, Hu & Cercone, 2003;Zhao, 2007;Zhao & Yao, 2005). Generally, an interactive data mining system is an integration of a human user and a computer.…”

Exploring wind dynamics in space and time : mining frequent pattern and modelling power potential

“…Data mining (DM) techniques have been used in various studies for exploring patterns from complex and large datasets where it has been found to be less time consuming than other approaches (Zaki et al 2010). DM refers to the process of extracting meaningful patterns that were previously unknown and "hidden" in the data (Baker 2010, Zhao andYao 2005). In a context of ever-increasing wind data and with pressing needs for turning this data into useful information (e.g.…”