Abstract High dimensional data becomes very common with the rapid growth of data that has been stored in databases or other information areas. Thus clustering process became an urgent problem. The well-known clustering algorithms are not adequate for the high dimensional space because of the problem that is called curse of dimensionality. So dimensionality reduction techniques have been used for accurate clustering results and improve the clustering time in high dimensional space. In this work different dimensionality reduction techniques were combined with Fuzzy C-Means clustering algorithm. It is aimed to reduce the complexity of high dimensional datasets and to generate more accurate clustering results. The results were compared in terms of cluster purity, cluster entropy and mutual info. Dimension reduction techniques are compared with current Central Processing Unit (CPU), current memory and elapsed CPU time. The experiments showed that the proposed work produces promising results on high dimensional space.

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[48] Zhou T., Tao D., and Wu X., Manifold Elasticnet: a Unified Framework for Sparse Dimension Reduction, Data Mining and Knowledge Discovery, vol. 22, no.3, pp. 340-371, 2011. Kazim Yildiz received a Ph.D and Msc in Electronic and Computer Education (2014) and (2010) respectively in Marmara University. He received a B.Sc. degree in Computer and Control Education from Marmara University. From August 2009 to August 2015, Kazim Yildiz worked as a research assistant. From August 2015 he has been working as an Assistant Professor in Computer Engineering department of Technology Faculty. His current research areas are digital image processing, high dimensional data mining and thermal imaging. Buket Dogan received the MS and PhD degrees in Computer-Control Educationfrom Marmara University in 2001 and 2006, respectively.From 1999 to 2007 she worked as a research assistant. She has been working as an Assistant Professor in Computer Engineering department of Technology Faculty. Her research interests include data mining, image processing and adaptive web based educational systems. Yilmaz Camurcu received the PhD degree in computer education fromMarmara University, Istanbul in 1996. He is a professor of Computer Engineering in the Faculty of Engineeringand Architecture at Fatih Sultan Mehmet Waqf University. He is a member of ACM. His current research interests are data mining, intelligent tutoring systems, and medical image processing.