Browsing by Author "Yildirim, Ahmet Artu"

Now showing 1 - 2 of 2

Citation - WoS: 6
Citation - Scopus: 16
Parallel Wavecluster: a Linear Scaling Parallel Clustering Algorithm Implementation With Application To Very Large Datasets
(Academic Press inc Elsevier Science, 2011) Ozdogan, Cem; Yildirim, Ahmet Artu
A linear scaling parallel clustering algorithm implementation and its application to very large datasets for cluster analysis is reported. WaveCluster is a novel clustering approach based on wavelet transforms. Despite this approach has an ability to detect clusters of arbitrary shapes in an efficient way, it requires considerable amount of time to collect results for large sizes of multi-dimensional datasets. We propose the parallel implementation of the WaveCluster algorithm based on the message passing model for a distributed-memory multiprocessor system. In the proposed method, communication among processors and memory requirements are kept at minimum to achieve high efficiency. We have conducted the experiments on a dense dataset and a sparse dataset to measure the algorithm behavior appropriately. Our results obtained from performed experiments demonstrate that developed parallel WaveCluster algorithm exposes high speedup and scales linearly with the increasing number of processors. (C) 2011 Elsevier Inc. All rights reserved.
Citation - WoS: 5
Citation - Scopus: 13
Parallel Wavelet-Based Clustering Algorithm on Gpus Using Cuda
(Elsevier Science Bv, 2011) Ozdogan, Cem; Yildirim, Ahmet Artu
There has been a substantial interest in scientific and engineering computing community to speed up the CPU-intensive tasks on graphical processing units (GPUs) with the development of many-core GPUs as having very large memory bandwidth and computational power. Cluster analysis is a widely used technique for grouping a set of objects into classes of "similar" objects and commonly used in many fields such as data mining, bioinformatics and pattern recognition. WaveCluster defines the notion of cluster as a dense region consisting of connected components in the transformed feature space. In this study, we present the implementation of WaveCluster algorithm as a novel clustering approach based on wavelet transform to GPU level parallelization and investigate the parallel performance for very large spatial datasets. The CUDA implementations of two main sub-algorithms of WaveCluster approach; namely extraction of low-frequency component from the signal using wavelet transform and connected component labeling are presented. Then, the corresponding performance evaluations are reported for each sub-algorithm. Divide and conquer approach is followed on the implementation of wavelet transform and multi-pass sliding window approach on the implementation of connected component labeling. The maximum achieved speedup is found in kernel as 107x in the computation of extraction of the low-frequency component and 6x in the computation of connected component labeling with respect to the sequential algorithms running on the CPU. (C) 2010 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Guest Editor.