Selection of evolutionary approach based hybrid data mining algorithms for decision support systems and business intelligence

2012 ◽  
Author(s):  
Pardeep Kumar ◽  
Nitin ◽  
Durg Singh Chauhan ◽  
Vivek Kumar Sehgal
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Support Systems ◽  
Evolutionary Approach ◽  
Data Mining Algorithms ◽  
Hybrid Data ◽  
Mining Algorithms ◽  
Selection Of

scholarly journals A Survey on Data Mining Algorithms and Techniques in Medicine

2017 ◽  
Vol 1 (3) ◽  
pp. 61 ◽  
Author(s):  
Kasra Madadipouya
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Performance Metrics ◽  
Support Systems ◽  
Medical Decision ◽  
Medical Decision Support ◽  
Data Mining Algorithms ◽  
Medical Decision Support Systems ◽  
Mining Algorithms

Medical Decision Support Systems (MDSS) industry collects a huge amount of data, which is not properly mined and not put to the optimum use. This data may contain valuable information that awaits extraction. The knowledge may be encapsulated in various patterns and regularities that may be hidden in the data. Such knowledge may prove to be priceless in future medical decision making. Available medical decision support systems are based on static data, which may be out of date. Thus, a medical decision support system that can learn the relationships between patient histories, diseases in the population, symptoms, pathology of a disease, family history, and test results, would be useful to physicians and hospitals. This paper provides an in-depth review of available data mining algorithms and techniques. In addition to that, data mining applications in medicine are discussed as well as techniques for evaluating them and available applications of performance metrics.

Business Intelligence and Data Mining

2013 ◽  
pp. 57-70
Author(s):  
Zsolt T. Kardkovács
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Strategic Planning ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Business Processes ◽  
Support Systems ◽  
Decision Makers ◽  
Key Factors ◽  
Short Introduction

Whenever decision makers find out that they want to know more about how the business works and progresses, or why customers do what they do, then data miners are summoned, and business intelligence is to be built or altered. Data mining aims at retrieving valid, interesting, explicable connection between key factors for either operative reporting or supporting strategic planning. While data mining discovers static connections between factors, business intelligence visualizes relevant data for decision makers in order to make them identify fast changes and analyze precisely business states. In this chapter, the authors give a short introduction for data oriented decision support systems with data mining and business intelligence in it. While these techniques are widely used in business processes, there are much more bad practices than good ones. We try to make an attempt to demystify and clear the myths about these technologies, and determine who should and how (not) to use them.

scholarly journals Seleksi Penyerang Utama Menggunakan K-Means Clustering Dan Sistem Pendukung Keputusan Metode Topsis

2017 ◽  
Vol 2 (1) ◽  
pp. 37-48
Author(s):  
Renenata Ardilesmana Siregar
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Selection Process ◽  
Ideal Solution ◽  
Major Player ◽  
The Right ◽  
Selection Of ◽  
The Ideal

Untuk  menentukan penyerang ideal dalam sepak bola agar sesuai karakter dan kriteria yang diharapkan,  diperlukan  pelatih  yang  mempunyai naluri  tajam  dan  juga  sistem  yang  bisa membantu pelatih dalam memberikan pilihan. Biasanya dalam proses penentuan pemain masih dilakukan  secara  manual dengan melihat dari karakter dan kriteria dari pemain tersebut. Tetapi terkadang hanya dengan melihat dari karakter dan kriteria dari pemain tersebut saja masih kurang cukup sehingga jauh dari apa yang diharapkan. Untuk  mempermudah dalam pemilihan penyerang ideal, maka diperlukan suatu sistem yang dapat membantu pelatih untuk  memilih penyerang yang dibutuhkan sesuai dengan kebutuhan tim yaitu dengan menggunakan teknik K-Means Clustering dalam metode data mining sebagai proses dalam menyeleksi pemain untuk bergabung  dalam  suatu  tim  dan  juga  didukung  dengan  metode  Sistem  Pendukung Keputusan (Decision Support Systems) The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) sebagai proses dalam menentukan penyerang yang akan bermain sebagai pemain utama dalam tim yang menggunakan beberapa kriteria untuk  memilih pemain yang tepat. Dengan hasil penelitian ini, diharapkan dapat membantu pelatih dalam proses seleksi pemain dan dapat mengubah cara penilaian terhadap sifat subjektif agar lebih obyektif dalam pengambilan keputusan. Kata Kunci :Data Mining, K-Means Clustering, Sistem Pendukung Keputusan To  determine  the  ideal  attacker  in football to match the expected character and criteria, a  coach who has a sharp instinct and a system that can assist the coach in providing choices. Usually  in  the  process  of  determining  the  player  is  still  done  manually  by  look ing  at  the characters  and  criteria  of  the  player.  But  sometimes just by look ing at the characters and criteria of the player is still not enough so far from what is expected. To facilitate the selection of ideal attackers, a system that can help the trainer to select the attacker needed according to the needs of the team is by using K-Means Clustering technique in the method of data mining as a process in selecting players to join a team and also supported by Decision Support Systems method The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is the process of determining which attack er will play as a major player in the team using multiple criteria to select the right player. With the results of this study, it is expected to assist trainers in the selection process of players and can change the way the assessment of the subjective nature to be more objective in decision making. Keywords: Data Mining, K-Means Clustering, Decision Support System.  

A Perspective on Data Mining Integration with Business Intelligence

Data Mining ◽  
2013 ◽  
pp. 1873-1892
Author(s):  
Ana Azevedo ◽  
Manuel Filipe Santos
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Support Systems ◽  
Underlying Structure ◽  
The Past ◽  
Definition Of

Business Intelligence (BI) is an emergent area of the Decision Support Systems (DSS) discipline. Over the past years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. The purpose of this chapter is to discuss the relevance of DM integration with BI, and its importance to business users. From the literature review, it was observed that the definition of an underlying structure for BI is missing, and therefore a framework is presented. It was also observed that some efforts are being done that seek the establishment of standards in the DM field, both by academics and by people in the industry. Supported by those findings, this chapter introduces an architecture that can conduct to an effective usage of DM in BI. This architecture includes a DM language that is iterative and interactive in nature. This chapter suggests that the effective usage of DM in BI can be achieved by making DM models accessible to business users, through the use of the presented DM language.

Extending QMBE Language with Clustering

2013 ◽  
Vol 5 (4) ◽  
pp. 59-77 ◽  
Author(s):  
Ana Azevedo ◽  
Manuel Filipe Santos
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Support Systems ◽  
Classification Rules ◽  
The Past

Business Intelligence (BI) is an important area of the Decision Support Systems (DSS) discipline. Over the past years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. This creates a gap between DM and BI systems. With the purpose of closing this gap a new DM language for BI, named as Query-Models-By-Example (QMBE), was envisaged and implemented with success, but addressing only classification rules. This paper presents an extension of QMBE language to include clustering. This represents one more step towards the integration of DM with BI, which constitutes an important issue.

scholarly journals Web Mining for the Integration of Data Mining with Business Intelligence in Web-Based Decision Support Systems

2015 ◽  
pp. 120-142 ◽  
Author(s):  
Marcos Aurélio Domingues ◽  
Alípio Mário Jorge ◽  
Carlos Soares ◽  
Solange Oliveira Rezende
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Web Mining ◽  
Support Systems ◽  
Web Documents ◽  
Web Based ◽  
Use Of Data ◽  
Computer Based

Web mining can be defined as the use of data mining techniques to automatically discover and extract information from web documents and services. A decision support system is a computer-based information system that supports business or organizational decision-making activities. Data mining and business intelligence techniques can be integrated in order to develop more advanced decision support systems. In this chapter, the authors propose to use web mining as a process to develop advanced decision support systems in order to support the management activities of a website. They describe the web mining process as a sequence of steps for the development of advanced decision support systems. By following such a sequence, the authors can develop advanced decision support systems, which integrate data mining with business intelligence, for websites.

A Perspective on Data Mining Integration with Business Intelligence

2010 ◽  
pp. 109-129 ◽  
Author(s):  
Ana Azevedo ◽  
Manuel Filipe Santos
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Support Systems ◽  
Underlying Structure ◽  
The Past ◽  
Definition Of

Business Intelligence (BI) is an emergent area of the Decision Support Systems (DSS) discipline. Over the past years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. The purpose of this chapter is to discuss the relevance of DM integration with BI, and its importance to business users. From the literature review, it was observed that the definition of an underlying structure for BI is missing, and therefore a framework is presented. It was also observed that some efforts are being done that seek the establishment of standards in the DM field, both by academics and by people in the industry. Supported by those findings, this chapter introduces an architecture that can conduct to an effective usage of DM in BI. This architecture includes a DM language that is iterative and interactive in nature. This chapter suggests that the effective usage of DM in BI can be achieved by making DM models accessible to business users, through the use of the presented DM language.

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