Data Mining for Organizations: Challenges and Opportunities for Small Developing States

2015 ◽  
pp. 208-233
Keyword(s):  
Data Mining ◽  
Challenges And Opportunities

A Classification Framework for Data Mining Applications in Criminal Science and Investigations

2020 ◽  
pp. 277-293
Author(s):  
Mahima Goyal ◽  
Vishal Bhatnagar ◽  
Arushi Jain
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Mining Techniques ◽  
Road Map ◽  
Classification Framework ◽  
Challenges And Opportunities ◽  
Crucial Information ◽  
Tools And Techniques ◽  
Day By Day ◽  
Mining Tools

The importance of data analysis across different domains is growing day by day. This is evident in the fact that crucial information is retrieved through data analysis, using different available tools. The usage of data mining as a tool to uncover the nuggets of critical and crucial information is evident in modern day scenarios. This chapter presents a discussion on the usage of data mining tools and techniques in the area of criminal science and investigations. The application of data mining techniques in criminal science help in understanding the criminal psychology and consequently provides insight into effective measures to curb crime. This chapter provides a state-of-the-art report on the research conducted in this domain of interest by using a classification scheme and providing a road map on the usage of various data mining tools and techniques. Furthermore, the challenges and opportunities in the application of data mining techniques in criminal investigation is explored and detailed in this chapter.

Data Mining in Protein Identification by Tandem Mass Spectrometry

2011 ◽  
pp. 472-478
Author(s):  
Haipeng Wang
Keyword(s):  
Mass Spectrometry ◽  
Data Mining ◽  
Tandem Mass Spectrometry ◽  
Large Scale ◽  
Protein Identification ◽  
Peptide Fragmentation ◽  
Tandem Mass ◽  
Fragmentation Pattern ◽  
Post Translational Modification ◽  
Challenges And Opportunities

Protein identification (sequencing) by tandem mass spectrometry is a fundamental technique for proteomics which studies structures and functions of proteins in large scale and acts as a complement to genomics. Analysis and interpretation of vast amounts of spectral data generated in proteomics experiments present unprecedented challenges and opportunities for data mining in areas such as data preprocessing, peptide-spectrum matching, results validation, peptide fragmentation pattern discovery and modeling, and post-translational modification (PTM) analysis. This article introduces the basic concepts and terms of protein identification and briefly reviews the state-of-the-art relevant data mining applications. It also outlines challenges and future potential hot spots in this field.

Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications

2008 ◽  
pp. 26-49 ◽  
Author(s):  
Yong Shi ◽  
Yi Peng ◽  
Gang Kou ◽  
Zhengxin Chen
Keyword(s):  
Data Mining ◽  
Linear Programming ◽  
Credit Card ◽  
Programming Model ◽  
Real Life ◽  
Multiple Criteria ◽  
Linear Programming Model ◽  
Multiple Criteria Optimization ◽  
Research Challenges ◽  
Challenges And Opportunities

This chapter provides an overview of a series of multiple criteria optimization-based data mining methods, which utilize multiple criteria programming (MCP) to solve data mining problems, and outlines some research challenges and opportunities for the data mining community. To achieve these goals, this chapter first introduces the basic notions and mathematical formulations for multiple criteria optimization-based classification models, including the multiple criteria linear programming model, multiple criteria quadratic programming model, and multiple criteria fuzzy linear programming model. Then it presents the real-life applications of these models in credit card scoring management, HIV-1 associated dementia (HAD) neuronal dam-age and dropout, and network intrusion detection. Finally, the chapter discusses research challenges and opportunities.

Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications

2009 ◽  
pp. 1-25
Author(s):  
Yong Shi ◽  
Yi Peng ◽  
Gang Kou ◽  
Zhengxin Chen
Keyword(s):  
Data Mining ◽  
Linear Programming ◽  
Programming Model ◽  
Neuronal Damage ◽  
Real Life ◽  
Multiple Criteria ◽  
Linear Programming Model ◽  
Multiple Criteria Optimization ◽  
Research Challenges ◽  
Challenges And Opportunities

This chapter provides an overview of a series of multiple criteria optimization-based data mining methods, which utilize multiple criteria programming (MCP) to solve data mining problems, and outlines some research challenges and opportunities for the data mining community. To achieve these goals, this chapter first introduces the basic notions and mathematical formulations for multiple criteria optimization- based classification models, including the multiple criteria linear programming model, multiple criteria quadratic programming model, and multiple criteria fuzzy linear programming model. Then it presents the real-life applications of these models in credit card scoring management, HIV-1 associated dementia (HAD) neuronal damage and dropout, and network intrusion detection. Finally, the chapter discusses research challenges and opportunities.

Data Mining

2011 ◽  
pp. 103-112 ◽  
Author(s):  
Sufi M. Nazem ◽  
Bongsik Shin
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Management Strategies ◽  
Relevant Information ◽  
Early Years ◽  
Data Warehouses ◽  
Strategic Decisions ◽  
Business Opportunities ◽  
Challenges And Opportunities ◽  
New Business

During the early years of database management the contemporary wisdom was to store only ‘useful data.’ In large part, this philosophy was encouraged because of the then-limited storage capacity offered by the prevailing technology. Then along came the microprocessor revolution, enormously expanding the scope of data storage. Subsequent advancement in information technology and recognition of potential business opportunities thereof, resulted in enormous expansion of data storage. Although the future is unknown and unpredictable, it often provides new business opportunities. Thus, new management strategies emerged which encouraged the massive accumulation of data and thus the advent of data warehousing. These massive data depositories are now providing both challenges and opportunities for strategic decision-making concerned with improving existing businesses and exploring new business opportunities. Data mining is an essential part of the process involved in locating relevant information from data warehouses for use in making such strategic decisions. Naturally, business leaders everywhere are willing to make investments in corporate data warehouses to enhance their access to information. The return on such investment is by no means guaranteed but all business activities include a certain amount of risk.

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