scholarly journals New Patterns And Techniques In Knowledge Discovery Through Data Mining

2013 ◽  
Vol 7 (1) ◽  
pp. 947-954
Author(s):  
Tiruveedula Gopi Krishna ◽  
Dr.Mohamed Abdeldaiem Abdelhadi ◽  
M.Madhusudhana Subramanian
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Knowledge Discovery ◽  
Large Datasets ◽  
Automatic Process ◽  
Surface Complex ◽  
Structural Patterns ◽  
Very Large Datasets ◽  
Complex Relationships ◽  
Decision Support Process

The main focus of this paper discussion was on mining and its set of techniques used in an automated approach to exhaustively explore and bring to the surface complex relationships in very large datasets. We discussed how a decision support process can be used to search for patterns of information in data. And also discussed different techniques for finding and describing structural patterns in data as well. Knowledge Discovery is a concept that describes the process of automatically searching large volumes of data for patterns that can be considered knowledge about the data. We discussed all automatic and semi-automatic process of discovering the patterns in data that how it leads to some advantage in businesses to make knowledge driven decisions, which help the company to succeed and compete.

Knowledge Discovery and Data Mining Applications in the Healthcare Industry

2015 ◽  
pp. 241-262
Author(s):  
Iman Barazandeh ◽  
Mohammad Reza Gholamian
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support System ◽  
Knowledge Discovery ◽  
Support System ◽  
Healthcare Industry ◽  
Actionable Knowledge ◽  
System View ◽  
Data Market

The healthcare industry is one of the most attractive domains to realize the actionable knowledge discovery objectives. This chapter studies recent researches on knowledge discovery and data mining applications in the healthcare industry and proposes a new classification of these applications. Studies show that knowledge discovery and data mining applications in the healthcare industry can be classified to three major classes, namely patient view, market view, and system view. Patient view includes papers that performed pure data mining on healthcare industry data. Market view includes papers that saw the patients as customers. System view includes papers that developed a decision support system. The goal of this classification is identifying research opportunities and gaps for researchers interested in this context.

Formal Knowledge Engineering Methods for Knowledge Discovery

2002 ◽  
Vol 01 (02) ◽  
pp. 141-154
Author(s):  
Satheesh Ramachandran
Keyword(s):  
Data Mining ◽  
Knowledge Management ◽  
Life Cycle ◽  
Decision Support ◽  
Knowledge Discovery ◽  
Knowledge Engineering ◽  
The Other ◽  
Full Potential ◽  
Formal Knowledge

This paper presents a framework for the integrated use of formal knowledge engineering methods and data mining based knowledge discovery methods. Knowledge is a key enterprise asset, and organizations are adopting both knowledge engineering and knowledge discovery paradigms for better knowledge management and enhanced decision support capability. Although there exists a useful interdependence between these endeavors, not much effort has been focused on using the full potential of one for the other. This paper presents a framework for the integrated use of established formal knowledge engineering methods and knowledge discovery processes with the ultimate intent of better managing the enterprise knowledge life cycle. It provides a brief overview of the knowledge discovery processes, and introduces a class of formal knowledge engineering methods and the perceived role of these methods in supporting the integration between the two worlds of knowledge discovery and knowledge engineering.

Sequential Patterns Postprocessing for Structural Relation Patterns Mining

2011 ◽  
pp. 216-234
Author(s):  
Lu Jing ◽  
Chen Weiru ◽  
Adjei Osei ◽  
Keech Malcolm
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Efficient Method ◽  
Sequential Patterns ◽  
Data Mining Technique ◽  
Structural Patterns ◽  
Structural Relation ◽  
Integrative Framework ◽  
Complex Structural ◽  
Over Time

Sequential patterns mining is an important data-mining technique used to identify frequently observed sequential occurrence of items across ordered transactions over time. It has been extensively studied in the literature, and there exists a diversity of algorithms. However, more complex structural patterns are often hidden behind sequences. This article begins with the introduction of a model for the representation of sequential patterns—Sequential Patterns Graph—which motivates the search for new structural relation patterns. An integrative framework for the discovery of these patterns–Postsequential Patterns Mining–is then described which underpins the postprocessing of sequential patterns. A corresponding data-mining method based on sequential patterns postprocessing is proposed and shown to be effective in the search for concurrent patterns. From experiments conducted on three component algorithms, it is demonstrated that sequential patterns-based concurrent patterns mining provides an efficient method for structural knowledge discovery.

Mining Big Data and Streams

2018 ◽  
pp. 406-417
Author(s):  
Hoda Ahmed Abdelhafez
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Large Datasets ◽  
Lessons Learned ◽  
Velocity Data ◽  
Time Data ◽  
Data Mining Techniques ◽  
Complex Information ◽  
Very Large Datasets ◽  
Data Volume

Mining big data is getting a lot of attention currently because the businesses need more complex information in order to increase their revenue and gain competitive advantage. Therefore, mining the huge amount of data as well as mining real-time data needs to be done by new data mining techniques/approaches. This chapter will discuss big data volume, variety and velocity, data mining techniques and open source tools for handling very large datasets. Moreover, the chapter will focus on two industrial areas telecommunications and healthcare and lessons learned from them.

Mining Big Data and Streams

2019 ◽  
pp. 94-107
Author(s):  
Hoda Ahmed Abdelhafez
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Large Datasets ◽  
Lessons Learned ◽  
Velocity Data ◽  
Time Data ◽  
Data Mining Techniques ◽  
Complex Information ◽  
Very Large Datasets ◽  
Data Volume

Mining big data is getting a lot of attention currently because businesses need more complex information in order to increase their revenue and gain competitive advantage. Therefore, mining the huge amount of data as well as mining real-time data needs to be done by new data mining techniques/approaches. This chapter will discuss big data volume, variety, and velocity, data mining techniques, and open source tools for handling very large datasets. Moreover, the chapter will focus on two industrial areas telecommunications and healthcare and lessons learned from them.

Data Mining Approach to Decision Support in Social Welfare

2018 ◽  
pp. 1783-1802
Author(s):  
Ricardo Anderson ◽  
Gunjan Mansingh
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Social Welfare ◽  
Knowledge Discovery ◽  
Support System ◽  
Process Model ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Data Mining Approach

Knowledge discovery and data-mining techniques have the potential to provide insights into data that can improve decision making. This paper explores the use of data mining to extract patterns from data in the domain of social welfare. It discusses the application of the Integrated Knowledge Discovery and Data Mining process model (IKDDM) a social welfare programme in Jamaica. Further, it demonstrates how the knowledge acquired from the data is used to develop a knowledge driven decision support system (DSS) in the PATH CCT programme. This system was successfully tested in the domain showing over 94% accuracy in the comparative decisions produced.

Knowledge Discovery and Data Mining Applications in the Healthcare Industry

2016 ◽  
pp. 1097-1118 ◽  
Author(s):  
Iman Barazandeh ◽  
Mohammad Reza Gholamian
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support System ◽  
Knowledge Discovery ◽  
Support System ◽  
Healthcare Industry ◽  
Actionable Knowledge ◽  
System View ◽  
Data Market

The healthcare industry is one of the most attractive domains to realize the actionable knowledge discovery objectives. This chapter studies recent researches on knowledge discovery and data mining applications in the healthcare industry and proposes a new classification of these applications. Studies show that knowledge discovery and data mining applications in the healthcare industry can be classified to three major classes, namely patient view, market view, and system view. Patient view includes papers that performed pure data mining on healthcare industry data. Market view includes papers that saw the patients as customers. System view includes papers that developed a decision support system. The goal of this classification is identifying research opportunities and gaps for researchers interested in this context.

Sequential Patterns Postprocessing for Structural Relation Patterns Mining

2010 ◽  
pp. 787-806
Author(s):  
Jing Lu ◽  
Weiru Chen ◽  
Osei Adjei ◽  
Malcolm Keech
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Efficient Method ◽  
Sequential Patterns ◽  
Data Mining Technique ◽  
Structural Patterns ◽  
Structural Relation ◽  
Integrative Framework ◽  
Complex Structural ◽  
Over Time

Sequential patterns mining is an important data-mining technique used to identify frequently observed sequential occurrence of items across ordered transactions over time. It has been extensively studied in the literature, and there exists a diversity of algorithms. However, more complex structural patterns are often hidden behind sequences. This article begins with the introduction of a model for the representation of sequential patterns—Sequential Patterns Graph—which motivates the search for new structural relation patterns. An integrative framework for the discovery of these patterns–Postsequential Patterns Mining–is then described which underpins the postprocessing of sequential patterns. A corresponding data-mining method based on sequential patterns postprocessing is proposed and shown to be effective in the search for concurrent patterns. From experiments conducted on three component algorithms, it is demonstrated that sequential patterns-based concurrent patterns mining provides an efficient method for structural knowledge discovery.

Knowledge Discovery and Data Mining Applications in the Healthcare Industry

2018 ◽  
pp. 2161-2182
Author(s):  
Iman Barazandeh ◽  
Mohammad Reza Gholamian
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support System ◽  
Knowledge Discovery ◽  
Support System ◽  
Healthcare Industry ◽  
Actionable Knowledge ◽  
System View ◽  
Data Market

The healthcare industry is one of the most attractive domains to realize the actionable knowledge discovery objectives. This chapter studies recent researches on knowledge discovery and data mining applications in the healthcare industry and proposes a new classification of these applications. Studies show that knowledge discovery and data mining applications in the healthcare industry can be classified to three major classes, namely patient view, market view, and system view. Patient view includes papers that performed pure data mining on healthcare industry data. Market view includes papers that saw the patients as customers. System view includes papers that developed a decision support system. The goal of this classification is identifying research opportunities and gaps for researchers interested in this context.

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