A clustering framework for unbalanced partitioning and outlier filtering on high dimensional datasets
11th East European Conference on Advances in Databases and Information Systems, ADBIS 2007, Varna, Bulgaristan, 29 Eylül - 03 Ekim 2007, cilt.4690 LNCS, ss.205-216, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Cilt numarası: 4690 LNCS
- Doi Numarası: 10.1007/978-3-540-75185-4_16
- Basıldığı Şehir: Varna
- Basıldığı Ülke: Bulgaristan
- Sayfa Sayıları: ss.205-216
- Anahtar Kelimeler: Clustering, Data mining, Dimensionality, Outlier filtering
- Maltepe Üniversitesi Adresli: Evet
Özet
In this study, we propose a better relationship based clustering framework for dealing with unbalanced clustering and outlier filtering on high dimensional datasets. Original relationship based clustering framework is based on a weighted graph partitioning system named METIS. However, it has two major drawbacks: no outlier filtering and forcing clusters to be balanced. Our proposed framework uses Graclus, an unbalanced kernel k-means based partitioning system. We have two major improvements over the original framework: First, we introduce a new space. It consists of tiny unbalanced partitions created using Graclus, hence we call it micro-partition space. We use a filtering approach to drop out singletons or micro-partitions that have fewer members than a threshold value. Second, we agglomerate the filtered micro-partition space and apply Graclus again for clustering. The visualization of the results has been carried out by CLUSION. Our experiments have shown that our proposed framework produces promising results on high dimensional datasets. © Springer-Verlag Berlin Heidelberg 2007.