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Kullanıcı Merkezli İnteraktif Veri Madenciliği: Bir Literatür Taraması

User-Centered Interactive Data Mining: A Literature Review

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Abstract (2. Language): 
Nowadays many data mining models concentrate on efficiency of the data analysis process and finding the patterns remained hidden in databases autonomously. However, only the use of data mining models for recognition of the patterns is not an appropriate approach because the patterns obtained can be too many but not useful for human users. Moreover, the knowledge which is useful for one person may not be useful to another person. For these reasons, it is necessary to apply a user-centered interactive approach to data mining process. Adaptive and effective communications are occurred between human users and computer systems and human users are able to determine the most appropriate data mining algorithm through user-centered interactive data mining. Thus, data mining is not a heavy and hard process for human users anymore and human users are able to acquire the most appropriate knowledge which is useful for them. This paper includes a literature review related to user-centered interactive data mining. Studies in the literature, the results obtained from these studies, evaluations of the developed systems and recommendations are presented. It has been taken over that data mining used for knowledge discovery in databases should have an interactive structure and multiple visualization techniques.
Abstract (Original Language): 
Günümüzde birçok veri madenciliği modeli, veri tabanlarında gizli kalmış örüntülerin otomatik olarak elde edilmesi ve veri analiz sürecinin verimliliğine önem vermektedir. Ancak, örüntülerin sadece veri madenciliği algoritmaları ile keşfedilmesi doğru bir yaklaşım değildir. Çünkü keşfedilen örüntüler fazla, ancak kişiler için yararlı olmayabilir. Bununla birlikte bir kişi için faydalı olan özbilgi başka bir kişi için faydalı olmayabilir. Bu nedenlerden ötürü veri madenciliği sürecine kullanıcı merkezli interaktif bir yaklaşımın uygulanması gerekmektedir. Kullanıcı merkezli interaktif veri madenciliği modelleri aracılığıyla kullanıcılar ve bilgisayar sistemleri arasında uyarlanır ve etkin iletişim yapıları oluşturulmakta, kullanıcılara en uygun veri madenciliği algoritmasını tespit etme imkânı sağlanmaktadır. Böylece kullanıcılar için veri madenciliği sıkıntılı ve zorlu bir süreç olmaktan çıkarılmakta, kullanıcılara kendileri için en uygun özbilgileri keşfetme imkânı sağlanmaktadır. Bu çalışmada kullanıcı merkezli interaktif veri madenciliği ile ilgili literatür taraması yer almaktadır. Literatürde yer alan çalışmalar, bu çalışmalardan elde edilen sonuçlar, geliştirilen sistemlerin değerlendirmeleri ve öneriler sunulmuştur. Veri tabanlarında özbilgi keşfi için kullanılan veri madenciliğinin, interaktif bir yapıya ve çoklu görselleştirme tekniklerine sahip olması gerektiği ön plana çıkmıştır
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REFERENCES

References: 

[1] H. Akpınar,“Veri Tabanlarında Bilgi Keşfi ve Veri Madenciliği”,
Istanbul University Journal of the School of Business
Administration, 29(1), 1-22, 2000.
[2] Y. Zhao, Y. H. Chen, Y. Y. Yao, “User-Centered Interactive Data
Mining”, 5th IEEE International Conference on Cognitive
Informatics (IEEE ICCI’06), Beijing, China, 457-466, 2006.
[3] Y. Zhao, Y. Yao, “On Interactive Data Mining”, 2nd Indian
International Conference on Artificial Intelligence (IICAI’05),
Pune, India, 2444-2454, 2005.
[4] İnternet: İnsan Bilgisayar Etkileşimi, http://ceng.gazi.edu.tr/~
hkaracan/BM515_H1.pdf, 2009.
[5] İnternet: Data Mining: Concepts and Techniques, http://www.
ir.iit.edu/~dagr/DataMiningCourse/Spring2001/BookNotes/4lang.p
df, 2001.
[6] H. Sever, B. Oğuz, “Veritabanlarında Bilgi Keşfine Formel Bir
Yaklaşım, Kısım 1: Eşleştirme Sorguları ve Algoritmalar”,
Information World, 3(2), 173-204, 2002.
[7] A. S. Albayrak, Ş. K. Yılmaz, “Veri Madenciliği: Karar Ağacı
Algoritmaları ve İMKB Verileri Üzerine Bir Uygulama”, Süleyman
Demirel University The Journal of Faculty of Economics and
Administrative Sciences, 14(1), 1-52, 2009.
[8] T. Altay, Knowledge discovery in databases and data mining
techniques: An applied study, M.Sc Thesis, Marmara University,
Institute for Graduate Studies in Pure and Applied Sciences, 2005.
[9] K. Wang, S. Tong, B. Eynard, L. Roucoules, N. Matta, “Review on
Application of Data Mining in Product Design and Manufacturing”,
4th International Conference on Fuzzy Systems and Knowledge
Discovery (FSKD’07), Hainan, China, 613-618, 24-27August,
2007.
[10] C. Olaru, L. Wehenkel, “Data Mining”, IEEE Computer
Applications in Power, 12(3), 19-25, 1999.
[11] R. Jin, G. Yang, G. Agrawal, “Shared Memory Parallelization of
Data Mining Algorithms: Techniques, Programming Interface and BİLİŞİM TEKNOLOJİLERİ DERGİSİ, CİLT: 3, SAYI: 1, OCAK 2010 21
Performance”, IEEE Transactions on Knowledge and Data
Engineering, 17(1), 71-89, 2005.
[12] A. Kalikov, Veri madenciliği ve bir ticaret uygulaması, Yüksek
Lisans Tezi, Gazi Üniversitesi, Fen Bilimleri Enstitüsü, 2006.
[13] M. Chen, J. Han, P. S. Yu, “Data Mining: An Overview From a
Database Perspective”, IEEE Transactions on Knowledge and Data
Engineering, 8(6), 866-883, 1996.
[14] P. Booth, An Introduction to Human-Computer Interaction,
Lawrence Erlbaum Associates, Hove, UK, 1989.
[15] Q. Chen, X. Wu, X. Zhu, “OIDM: Online Interactive Data Mining”,
17th International Conference on Industrial and Engineering
Applications of Artificial Intelligence and Expert Systems
(IEA/AIE 2004), Ottowa, Canada, 66-76, 2004.
[16] A. S. Filho, H. E. Liesenberg, R. M. Barros, “Designing User
Interface for Web Interactive Systems”, 3rd IEEE Symposium on
Application-Specific Systems and Software Engineering
Technology (ASSET'00), Richarson, Texas, USA, 9-16, 2000.
[17] Q. Luo, “Advancing Knowledge Discovery and Data Mining”, 1st
International Workshop on Knowledge Discovery and Data
Mining (WKDD’08), Adelaide, South Australia, 3-5, 2008.
[18] F. V. Ficarra, M. C. Ficarra, “Interactive Systems, Design and
Heuristic Evaluation: The Importance of the Diachronic Vision”,
New Directions in Intelligent Interactive Multimedia, Springer,
Heidelberg, 625-634, 2008.
[19] İnternet: Ten Usability Heuristics, http://www.useit.com/papers/
heuristic/heuristic_list.html, 2005.
[20] İnternet: Güvenlik Sistemleri için Kullanıcı Arayüzü Tasarımı,
http://www.bilmuh.gyte.edu.tr/~ispinar/BIL673/ZSevklikull%20ay
uz-tas.pdf, 2004.
[21] İnternet: User Interface Design, http://ceng.gazi.edu.tr/~hkaracan/
BM515_H5.pdf, 2009.
[22] S. Zuffia, C. Brambillab, G. Berettac, P. Scalaa, “Human Computer
Interaction: Legibility and Contrast”, 14th International
Conference on Image Analysis and Processing (ICIAP’07),
Modena, Italy, 241-246, 2007.
[23] U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, “From Data Mining to
Knowledge Discovery in Databases”, AI Magazine, 17(3), 37-54,
1996.
[24] U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, “The KDD Process for
Extracting Useful Knowledge from Volumes of Data”,
Communications of the ACM, 39(11), 27-34, 1996.
[25] A. Lew, H. Mauch, “Introduction to Data Mining Principles”,
Introduction to Data Mining and Its Applications, Springer,
Heidelberg, 1-20, 2006.
[26] D. Guo, D. J. Peuquet, M. Gahegan, “ICEAGE: Interactive
Clustering and Exploration of Large and High-Dimensional
Geodata”, Geoinformatica, 7(3), 229-253, 2003.
[27] I. Pigeot, A. Blauth, F. Bry, “Interactive Analysis of HighDimensional Association Structures with Graphical Models”,
Metrika, 51(1), 53-65, 1998.
[28] D. Pallez, L. Brisson, T. Baccino, “Towards a Human Eye Behavior
Model by Applying Data Mining Techniques on Gaze Information
from IEC”, 3rd International Conference on Human Centered
Processes (HCP'2008), Delft, Netherlands, 51-64, 2008.
[29] P. Chmelar, L. Stryka, “Interactive Mining on Hierarchical Data”,
13th Conference STUDENT EEICT, Brno, Czech Republic, 410-
414, 2007.
[30] J. Han, “DBMiner: Interactive Mining of Multiple-Level
Knowledge in Large Relational Databases”, International
Conference on Management of Data (SIGMOD’96), Montreal,
Canada, 50-59, 1996.
[31] M. Ankerst, “Human Involvement and Interactivity of the Next
Generation’s Data Mining Tools”, International Workshop on
Research Issues in Data Mining and Knowledge Discovery
(DMKD’01), Santa Barbara, California, USA, 2001.
[32] İnternet: “Interactive Visual Data Mining”, http://www.scribd.com/
doc/15983592/FADLINREQ3BIdeagrouppublishingencyclopediaof
DatawarehousingandMiningjul2005eBookLinG, 2009.
[33] A. Pryke, R. Beale, “Haiku: Interactive Comprehensible Data
Mining”, Workshop on Ambient Intelligence for Scientific
Discovery (SIGCHI’04), Vienna, Austria, 2004.
[34] M. Chen, Q. Zhu, Z. Chen, “An Integrated Interactive Environment
for Knowledge Discovery from Heterogeneous Data Resources”,
Information and Software Technology, 43(8), 487-496, 2001.
[35] M. C. Oliveira, H. Levkowitz, “From Visual Data Exploration
to Visual Data Mining: A Survey”, IEEE Transactions on
Visualization and Computer Graphics, 9(3), 378-394, 2003.
[36] M. Ankerst, M. Ester, H. P. Kriegel, “Visual Classification: An
Interactive Approach to Decision Tree Construction”, 5th ACM
SIGKDD International Conference on Knowledge Discovery
and Data Mining, San Diego, California, USA, 392-396, 1999.
[37] J. Hellerstein, R. Avnur, A. Chou, C. Hidber, C. Olston, V. Raman,
T. Roth, P. Haas, “Interactive Data Analysis: The Control Project”,
IEEE Computer, 32(8), 51-59, 1999.
[38] V. Raman, B. Raman, J. M. Hellerstein, “Online Dynamic
Reordering for Interactive Data Processing”, 25th International
Conference on Very Large Data Bases, Edinburgh, UK, 709-720,
1999.
[39] R. Hübscher, S. Puntambekar, A. H. Nye, “Domain Specific
Interactive Data Mining”, 11th International Conference on User
Modeling (UM’07), Workshop on Data Mining for User
Modeling, Corfu, Greece, 81-90, 2007.
[40] J. Demsar, B. Zupan, G. Leban, T. Curk, “Orange: From
Experimental Machine Learning to Interactive Data Mining”,
Practice of Knowledge Discovery in Databases, Springer,
Heidelberg, 537-539, 2004.
[41] W. Hou, Y. Gu, D. Che, C. Luo, Z. Jiang, “StatMine: An
Interactive Statistical Data Mining System”, International Journal
of Computational Science, 2(1), 122-140, 2008.

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