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.

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.

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

scholarly journals Modelling of e-Governance Framework for Mining Knowledge from Massive Grievance Redressal Data

2017 ◽  
Vol 6 (1) ◽  
pp. 32
Author(s):  
Sangeetha G ◽  
L. Manjunatha Rao
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Knowledge Discovery ◽  
Discussion Forums ◽  
Discovery Process ◽  
Web Based ◽  
Governance Framework ◽  
Data Mining Algorithms ◽  
Massive Number ◽  
Mining Algorithms

With the massive proliferation of online applications for the citizens with abundant resources, there is a tremendous hike in usage of e-governance platforms. Right from entrepreneur, players, politicians, students, or anyone who are highly depending on web-based grievance redressal networking sites, which generates loads of massive grievance data that are not only challenging but also highly impossible to understand. The prime reason behind this is grievance data is massive in size and they are highly unstructured. Because of this fact, the proposed system attempts to understand the possibility of performing knowledge discovery process from grievance Data using conventional data mining algorithms. Designed in Java considering massive number of online e-governance framework from civilian’s grievance discussion forums, the proposed system evaluates the effectiveness of performing datamining for Big data.

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