The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Relevance feedback document retrieval using support vector machines
AM'03 Proceedings of the Second international conference on Active Mining
Micro view and macro view approaches to discovered rule filtering
AM'03 Proceedings of the Second international conference on Active Mining
Mining chemical compound structure data using inductive logic programming
AM'03 Proceedings of the Second international conference on Active Mining
Extracting diagnostic knowledge from hepatitis dataset by decision tree graph-based induction
AM'03 Proceedings of the Second international conference on Active Mining
Data mining oriented CRM systems based on MUSASHI: C-MUSASHI
AM'03 Proceedings of the Second international conference on Active Mining
Investigation of rule interestingness in medical data mining
AM'03 Proceedings of the Second international conference on Active Mining
Experimental evaluation of time-series decision tree
AM'03 Proceedings of the Second international conference on Active Mining
Spiral multi-aspect hepatitis data mining
AM'03 Proceedings of the Second international conference on Active Mining
Sentence role identification in medline abstracts: training classifier with structured abstracts
AM'03 Proceedings of the Second international conference on Active Mining
Empirical comparison of clustering methods for long time-series databases
AM'03 Proceedings of the Second international conference on Active Mining
Spiral mining using attributes from 3d molecular structures
AM'03 Proceedings of the Second international conference on Active Mining
Classification of pharmacological activity of drugs using support vector machine
AM'03 Proceedings of the Second international conference on Active Mining
Cooperative scenario mining from blood test data of hepatitis b and c
AM'03 Proceedings of the Second international conference on Active Mining
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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.