Interactive classification using a granule network
ICCI '05 Proceedings of the Fourth IEEE International Conference on Cognitive Informatics
3DM: Domain-oriented Data-driven Data Mining
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
Domain-Driven Data Mining: Methodologies and Applications
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
A wistech paradigm for intelligent systems
Transactions on rough sets VI
Toward perception based computing: a rough-granular perspective
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
Domain-oriented data-driven data mining (3DM): simulation of human knowledge understanding
WImBI'06 Proceedings of the 1st WICI international conference on Web intelligence meets brain informatics
Introduction to 3DM: domain-oriented data-driven data mining
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Knowledge theory and artificial intelligence
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
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We are living in an information technology (IT) era now. Advances in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data and information. What will be the next step of IT? Many researchers predict that the next step of IT might be Knowledge Technology (KT). KT refers to a fuzzy set of tools enabling better acquisition, representation, organization, exchange and application of information and knowledge. In this talk, we will address some issues about the development of IT to KT. Some KT related events happened in the past years [1-5], organizations of KT [6-8], and understandings of KT [9-12] will be introduced. One of the most important issues for developing KT, knowledge acquisition and data mining, will be discussed in a new view of translation [13, 14]. Some basic issues of data mining will be analyzed in this view. A new model of data mining, domain-oriented data-driven data mining (3DM), will be proposed [14-17]. The relationship between traditional domain-driven (or user-driven) data mining models [18-20] and our proposed 3DM model will also be analyzed [21]. Some domain-oriented data-driven data mining algorithms for mining such knowledge as default rule [22], decision tree [23], and concept lattice [24] from database will be introduced. The experiment results of these algorithms are also shown to illustrate the efficiency and performance of the knowledge acquired by 3DM data mining algorithms.