Towards human consistent data driven decision support systems using verbalization of data mining results via linguistic data summaries

2010 ◽  
Vol 58 (3) ◽  
pp. 359-370 ◽  
Author(s):  
J. Kacprzyk ◽  
S. Zadrożny
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Ease Of Use ◽  
Data Driven ◽  
Linguistic Data ◽  
Developing Area ◽  
Tools And Techniques ◽  
Data Summaries

Towards human consistent data driven decision support systems using verbalization of data mining results via linguistic data summariesWe present how the conceptually and numerically simple concept of a fuzzy linguistic database summary can be a very powerful tool for gaining much insight into the essence of data that may be relevant for a business activity. The use of linguistic summaries provides tools for the verbalization of data analysis (mining) results which, in addition to the more commonly used visualization e.g. via a GUI, graphical user interface, can contribute to an increased human consistency and ease of use. The results (knowledge) derived are in a simple, easily comprehensible linguistic form which can be effectively and efficiently employed for supporting decision makers via the data driven decision support system paradigm. Two new relevant aspects of the analysis are also outlined which was first initiated by the authors. First, following Kacprzyk and Zadrożny [1] comments are given on an extremely relevant aspect of scalability of linguistic summarization of data, using their new concept of a conceptual scalability that is crucial for large applications. Second, following Kacprzyk and Zadrożny [2] it is further considered how linguistic data summarization is closely related to some types of solutions used in natural language generation (NLG), which can make it possible to use more and more effective and efficient tools and techniques developed in this another rapidly developing area. An application of a computer retailer is outlined.

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.

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