Erratum: Data Mining and Knowledge Discovery for Big Data

2014 ◽  
pp. 305-308 ◽  
Author(s):  
Wesley W. Chu
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Knowledge Discovery

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.

scholarly journals Biologically Inspired Techniques for Data Mining

2014 ◽  
pp. 1-10 ◽  
Author(s):  
Shafiq Alam ◽  
Gillian Dobbie ◽  
Yun Sing Koh ◽  
Saeed ur Rehman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Particle Swarm Optimization ◽  
Information Extraction ◽  
Knowledge Discovery ◽  
Swarm Intelligence ◽  
Optimization Techniques ◽  
Biologically Inspired ◽  
Swarm Optimization ◽  
Hidden Knowledge

Knowledge Discovery and Data (KDD) mining helps uncover hidden knowledge in huge amounts of data. However, recently, different researchers have questioned the capability of traditional KDD techniques to tackle the information extraction problem in an efficient way while achieving accurate results when the amount of data grows. One of the ways to overcome this problem is to treat data mining as an optimization problem. Recently, a huge increase in the use of Swarm Intelligence (SI)-based optimization techniques for KDD has been observed due to the flexibility, simplicity, and extendibility of these techniques to be used for different data mining tasks. In this chapter, the authors overview the use of Particle Swarm Optimization (PSO), one of the most cited SI-based techniques in three different application areas of KDD, data clustering, outlier detection, and recommender systems. The chapter shows that there is a tremendous potential in these techniques to revolutionize the process of extracting knowledge from big data using these techniques.

Biologically Inspired Techniques for Data Mining

2016 ◽  
pp. 2275-2284
Author(s):  
Shafiq Alam ◽  
Gillian Dobbie ◽  
Yun Sing Koh ◽  
Saeed ur Rehman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Particle Swarm Optimization ◽  
Information Extraction ◽  
Knowledge Discovery ◽  
Swarm Intelligence ◽  
Optimization Techniques ◽  
Biologically Inspired ◽  
Swarm Optimization ◽  
Hidden Knowledge

Knowledge Discovery and Data (KDD) mining helps uncover hidden knowledge in huge amounts of data. However, recently, different researchers have questioned the capability of traditional KDD techniques to tackle the information extraction problem in an efficient way while achieving accurate results when the amount of data grows. One of the ways to overcome this problem is to treat data mining as an optimization problem. Recently, a huge increase in the use of Swarm Intelligence (SI)-based optimization techniques for KDD has been observed due to the flexibility, simplicity, and extendibility of these techniques to be used for different data mining tasks. In this chapter, the authors overview the use of Particle Swarm Optimization (PSO), one of the most cited SI-based techniques in three different application areas of KDD, data clustering, outlier detection, and recommender systems. The chapter shows that there is a tremendous potential in these techniques to revolutionize the process of extracting knowledge from big data using these techniques.

Modelling of E-Governance Framework for Mining Knowledge from Massive Grievance Redressal Data

2016 ◽  
Vol 6 (1) ◽  
pp. 367
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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