Abstract
Active mining is a new direction in the knowledge discovery process for real-world applications handling various kinds of data with actual user need. Our ability to collect data, be it in business, government, science, and perhaps personal, has been increasing at a dramatic rate, which we call “information flood”. However, our ability to analyze and understand massive data lags far behind our ability to collect them. The value of data is no longer in “how much of it we have”. Rather, the value is in how quickly and effectively can the data be reduced, explored, manipulated and managed. For this purpose, Knowledge Discovery and Data mining (KDD) emerges as a technique that extracts implicit, previously unknown, and potentially useful information (or patterns) from data. However, recent extensive studies and real world applications show that the following requirements are indispensable to overcome information flood: (1) identifying and collecting the relevant data from a huge information search space (active information collection), (2) mining useful knowledge from different forms of massive data efficiently and effectively (user-centered active data mining), and (3) promptly reacting to situation changes and giving necessary feedback to both data collection and mining steps (active user reaction). Active mining is proposed as a solution to these requirements, which collectively achieves the various mining need. By “collectively achieving” we mean that the total effect outperforms the simple add-sum effect that each individual effort can bring.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The kdd process for extracting useful knowledge from volumes of data. CACM 29, 27–34 (1996)
Onoda, T., Murata, H., Yamada, S.: Relevance feedback document retrieval using support vector machines. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds.) AM 2003. LNCS (LNAI), vol. 3430, pp. 59–73. Springer, Heidelberg (2005)
Kitamura, Y., Iida, A., Park, K.: Micro view and macro view approaches to discovered rule filtering. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds.) AM 2003. LNCS (LNAI), vol. 3430, pp. 74–91. Springer, Heidelberg (2005)
Geamsakul, W., Yoshida, T., Ohara, K., Motoda, H., Washio, T., Yokoi, H., Katsuhiko, T.: Extracting diagnostic knowledge from hepatitis dataset by decision tree graph-based induction. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds.) AM 2003. LNCS (LNAI), vol. 3430, pp. 126–151. Springer, Heidelberg (2005)
Yada, K., Hamuro, Y., Katoh, N., Washio, T., Fusamoto, I., Fujishima, D., Ikeda, T.: Data mining oriented crm systems based on musashi: C-musashi. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds.) AM 2003. LNCS (LNAI), vol. 3430, pp. 155–176. Springer, Heidelberg (2005)
Ohsaki, M., Kitaguchi, S., Yokoi, H., Yamaguchi, T.: Investigation of rule interestingness in medical data mining. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds.) AM 2003. LNCS (LNAI), vol. 3430, pp. 177–193. Springer, Heidelberg (2005)
Yamada, Y., Suzuki, E., Yokoi, H., Takabayashi, K.: Experimental evaluation of time-series decision tree. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds.) AM 2003. LNCS (LNAI), vol. 3430, pp. 194–214. Springer, Heidelberg (2005)
Ohshima, M., Okuno, T., Fujita, Y., Zhong, N., Dong, J., Yokoi, H.: Spiral multi-aspect hepatitis data mining. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds.) AM 2003. LNCS (LNAI), vol. 3430, pp. 215–241. Springer, Heidelberg (2005)
Shimbo, M., Yamasaki, T., Matsumoto, Y.: Sentence role identification in medline abstracts: Training classifier with structured abstracts. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds.) AM 2003. LNCS (LNAI), vol. 3430, pp. 242–261. Springer, Heidelberg (2005)
Hirano, S., Tsumoto, S.: Empirical comparison of clustering methods for long time-series databases. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds.) AM 2003. LNCS (LNAI), vol. 3430, pp. 275–294. Springer, Heidelberg (2005)
Okada, T., Yamakawa, M., Niitsuma, H.: Spiral mining using attributes from 3d molecular structures. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds.) AM 2003. LNCS (LNAI), vol. 3430, pp. 295–310. Springer, Heidelberg (2005)
Takahashi, Y., Nishikoori, K., Fujishima, S.: Classification of pharmacological activity of drugs using support vector machine. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds.) AM 2003. LNCS (LNAI), vol. 3430, pp. 311–320. Springer, Heidelberg (2005)
Nattee, C., Sinthupinyo, S., Numao, M., Okada, T.: Mining chemical compound structure data using inductive logic programming. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds.) AM 2003. LNCS (LNAI), vol. 3430, pp. 92–113. Springer, Heidelberg (2005)
Ohsawa, Y., Fujie, H., Saiura, A., Okazaki, N., Matsumura, N.: Cooperative scenario mining from blood test data of hepatitis b and c. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds.) AM 2003. LNCS (LNAI), vol. 3430, pp. 321–344. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (2005). Active Mining Project: Overview. In: Tsumoto, S., Yamaguchi, T., Numao, M., Motoda, H. (eds) Active Mining. Lecture Notes in Computer Science(), vol 3430. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11423270_1
Download citation
DOI: https://doi.org/10.1007/11423270_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26157-5
Online ISBN: 978-3-540-31933-7
eBook Packages: Computer ScienceComputer Science (R0)