scholarly journals Knowledge Discovery and interactive Data Mining in Bioinformatics - State-of-the-Art, future challenges and research directions

2014 ◽  
Vol 15 (S6) ◽  
Author(s):  
Andreas Holzinger ◽  
Matthias Dehmer ◽  
Igor Jurisica
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
State Of The Art ◽  
Research Directions ◽  
Interactive Data Mining ◽  
Future Challenges ◽  
Interactive Data

User-Centered Interactive Data Mining

2010 ◽  
pp. 110-125
Author(s):  
Yan Zhao ◽  
Yiyu Yao
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Computer Systems ◽  
User Requirements ◽  
Knowledge Representations ◽  
Interactive Data Mining ◽  
Interactive Data ◽  
Selection Of ◽  
User Centered

While many data mining models concentrate on automation and efficiency, interactive data mining models focus on adaptive and effective communications between human users and computer systems. User requirements and preferences play an important role in human-machine interactions, and guide the selection of knowledge representations, knowledge discovery operations and measurements, combined with explanations of mined patterns. This chapter discusses these fundamental issues based on a usercentered three-layer framework of interactive data mining.

Guiding Knowledge Discovery Through Interactive Data Mining

2011 ◽  
pp. 45-87 ◽  
Author(s):  
Aaron Ceglar ◽  
John Roddick ◽  
Paul Calder
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Domain Knowledge ◽  
Abstract Concepts ◽  
Data Repositories ◽  
Interactive Data Mining ◽  
Semantic Concepts ◽  
Interactive Mining ◽  
Interactive Data ◽  
Mining Algorithms

Knowledge discovery is the process of eliciting interesting knowledge from data repositories. Due to the inability of computers to understand abstract concepts, present mining algorithms do not adequately constrain the generation of rules to those that are of interest to the user. Interactive mining techniques aim to alleviate this problem by involving the user in the mining process, so that the user’s understanding of abstract semantic concepts and domain knowledge can guide the discovery process, resulting in accelerated mining with improved results. This chapter presents a discussion of the current state of interactive data mining research.

Ubiquitous Mining with Interactive Data Mining Agents

2009 ◽  
Vol 24 (6) ◽  
pp. 1018-1027
Author(s):  
Xin-Dong Wu ◽  
Xing-Quan Zhu ◽  
Qi-Jun Chen ◽  
Fei-Yue Wang
Keyword(s):  
Data Mining ◽  
Interactive Data Mining ◽  
Interactive Data

scholarly journals Real-time interactive data mining for chemical imaging information: application to automated histopathology

2013 ◽  
Vol 14 (1) ◽  
pp. 156 ◽  
Author(s):  
David Mayerich ◽  
Michael Walsh ◽  
Matthew Schulmerich ◽  
Rohit Bhargava
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Chemical Imaging ◽  
Interactive Data Mining ◽  
Interactive Data
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