Impact of ranked ordered feature list (ROFL) on classification with visual data mining techniques

2016 ◽  
Author(s):  
Mridu Sahu ◽  
Shreya Sharma ◽  
Vyom Raj ◽  
N. K. Nagwani ◽  
Shrish Verma
Keyword(s):  
Data Mining ◽  
Visual Data ◽  
Visual Data Mining ◽  
Data Mining Techniques ◽  
Feature List

Visual Data Mining for Collaborative Filtering

2017 ◽  
pp. 217-235 ◽  
Author(s):  
Marenglen Biba ◽  
Narasimha Rao Vajjhala ◽  
Lediona Nishani
Keyword(s):  
Data Mining ◽  
Collaborative Filtering ◽  
State Of The Art ◽  
The State ◽  
Visual Exploration ◽  
Visual Data ◽  
Visual Data Mining ◽  
Data Mining Techniques ◽  
Existing Data

This book chapter provides a state-of-the-art survey of visual data mining techniques used for collaborative filtering. The chapter begins with a discussion on various visual data mining techniques along with an analysis of the state-of-the-art visual data mining techniques used by researchers as well as in the industry. Collaborative filtering approaches are presented along with an analysis of the state-of-the-art collaborative filtering approaches currently in use in the industry. Visual data mining can provide benefit to existing data mining techniques by providing the users with visual exploration and interpretation of data. The users can use these visual interpretations for further data mining. This chapter dealt with state-of-the-art visual data mining technologies that are currently in use apart. The chapter also includes the key section of the discussion on the latest trends in visual data mining for collaborative filtering.

Visual Data Mining for Collaborative Filtering

2017 ◽  
pp. 1274-1292
Author(s):  
Marenglen Biba ◽  
Narasimha Rao Vajjhala ◽  
Lediona Nishani
Keyword(s):  
Data Mining ◽  
Collaborative Filtering ◽  
State Of The Art ◽  
The State ◽  
Visual Exploration ◽  
Visual Data ◽  
Visual Data Mining ◽  
Data Mining Techniques ◽  
Existing Data

This book chapter provides a state-of-the-art survey of visual data mining techniques used for collaborative filtering. The chapter begins with a discussion on various visual data mining techniques along with an analysis of the state-of-the-art visual data mining techniques used by researchers as well as in the industry. Collaborative filtering approaches are presented along with an analysis of the state-of-the-art collaborative filtering approaches currently in use in the industry. Visual data mining can provide benefit to existing data mining techniques by providing the users with visual exploration and interpretation of data. The users can use these visual interpretations for further data mining. This chapter dealt with state-of-the-art visual data mining technologies that are currently in use apart. The chapter also includes the key section of the discussion on the latest trends in visual data mining for collaborative filtering.

Exploring the underlying structure of haptic-based handwritten signatures using visual data mining techniques

2010 ◽  
Author(s):  
Nizar Sakr ◽  
Fawaz A. Alsulaiman ◽  
Julio J. Valdes ◽  
Abdulmotaleb El Saddik ◽  
Nicolas D. Georganas
Keyword(s):  
Data Mining ◽  
Underlying Structure ◽  
Visual Data ◽  
Visual Data Mining ◽  
Data Mining Techniques

Interactive Visual Data Mining

2008 ◽  
pp. 1638-1642
Author(s):  
Shouhong Wang ◽  
Hai Wang
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Visual Data ◽  
Visual Data Mining ◽  
Data Mining Techniques ◽  
General Proposition ◽  
Level Information ◽  
High Level ◽  
Interactive Visual Data Mining ◽  
Structure Of Knowledge

In the data mining field, people have no doubt that high level information (or knowledge) can be extracted from the database through the use of algorithms. However, a one-shot knowledge deduction is based on the assumption that the model developer knows the structure of knowledge to be deducted. This assumption may not be invalid in general. Hence, a general proposition for data mining is that, without human-computer interaction, any knowledge discovery algorithm (or program) will fail to meet the needs from a data miner who has a novel goal (Wang, S. & Wang, H., 2002). Recently, interactive visual data mining techniques have opened new avenues in the data mining field (Chen, Zhu, & Chen, 2001; de Oliveira & Levkowitz, 2003; Han, Hu & Cercone, 2003; Shneiderman, 2002; Yang, 2003).

Interactive Visual Data Mining

2011 ◽  
pp. 644-646
Author(s):  
Shouhong Wang ◽  
Hai Wang
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Visual Data ◽  
Visual Data Mining ◽  
Data Mining Techniques ◽  
General Proposition ◽  
Level Information ◽  
High Level ◽  
Interactive Visual Data Mining ◽  
Structure Of Knowledge

In the data mining field, people have no doubt that high level information (or knowledge) can be extracted from the database through the use of algorithms. However, a one-shot knowledge deduction is based on the assumption that the model developer knows the structure of knowledge to be deducted. This assumption may not be invalid in general. Hence, a general proposition for data mining is that, without human-computer interaction, any knowledge discovery algorithm (or program) will fail to meet the needs from a data miner who has a novel goal (Wang, S. & Wang, H., 2002). Recently, interactive visual data mining techniques have opened new avenues in the data mining field (Chen, Zhu, & Chen, 2001; de Oliveira & Levkowitz, 2003; Han, Hu & Cercone, 2003; Shneiderman, 2002; Yang, 2003).

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