A Novel Parallel Algorithm for Clustering Documents Based on the Hierarchical Agglomerative Approach

Aboutabl, Amal Elsayed; Elsayed, Mohamed Nour

doi:10.5121/ijcsit.2011.3211

Cited by 6 publications

(8 citation statements)

References 10 publications

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“…23 Aboutabl and Elsayed introduced a parallel algorithm that adopts a hierarchical document clustering approach. 1 Pizzuti and Talia proposed a parallel version of the AutoClass system based upon the Bayesian model for determining the optimal classes in large data sets. 27 Chen et al presented a Parallel Spectral Clustering in Distributed Systems.…”

Section: Reviews Of Clustering Methodsmentioning

confidence: 99%

Parallel N-Path Quantification Hierarchical K-Means Clustering Algorithm for Video Retrieval

Liao

Zhao

Zheng

et al. 2017

Int. J. Patt. Recogn. Artif. Intell.

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Using clustering method to detect useful patterns in large datasets has attracted considerable interest recently. The HKM clustering algorithm (Hierarchical K-means) is very e±cient in large-scale data analysis. It has been widely used to build visual vocabulary for large scale video/image retrieval system. However, the speed and even the accuracy of hierarchical K-means clustering algorithm still have room to be improved. In this paper, we propose a Parallel N-path quanti¯cation hierarchical K-means clustering algorithm which improves on the hierarchical K-means clustering algorithm in the following ways. Firstly, we replace the Euclidean kernel with the Hellinger kernel to improve the accuracy. Secondly, the Greedy N-best Paths Labeling method is adopted to improve the clustering accuracy. Thirdly, the multi-core processors-based parallel clustering algorithm is proposed. Our results con¯rm that the proposed clustering algorithm is much faster and more e®ective.

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Section: Reviews Of Clustering Methodsmentioning

confidence: 99%

Parallel N-Path Quantification Hierarchical K-Means Clustering Algorithm for Video Retrieval

Liao

Zhao

Zheng

et al. 2017

Int. J. Patt. Recogn. Artif. Intell.

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“…The storage medium is composed of two blocks, each of which is a conventional RAM with a sequential data access structure. The word size of the mask RAM corresponds to the memory's access width, while the word size of the tail element RAM matches the bit depth of the image frame [1,5].…”

Section: Fig 1 Model Of Neural Structur Based On the Pi Transformatio...mentioning

confidence: 99%

Architektoniczne, Strukturalne I Funkcjonalne Cechy Równoległo-Hierarchicznej Organizacji Pamięci

Timchenko,

Kokriatska,

Tverdomed

et al. 2024

IAPGOS

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Parallel hierarchical memory (PI memory) is a new type of memory that is designed to improve the performance of parallel computing systems. PI memory is composed of two blocks: a mask RAM and a tail element RAM. The mask RAM stores the masks that are used to encode the information, while the tail element RAM stores the actual information. The address block of the PI memory is responsible for generating the physical addresses of the cells where the tail elements and their masks are stored. The address block also stores the field of addresses where the array was written and associates this field of addresses with the corresponding external address used to write the array. The proposed address block structure is able to efficiently generate the physical addresses of the cells where the tail elements and their masks are stored. The address block is also able to store the field of addresses where the array was written and associate this field of addresses with the corresponding external address used to write the array. The proposed address block structure has been implemented in a prototype PI memory. The prototype PI memory has been shown to be able to achieve significant performance improvements over traditional memory architectures. The paper will present a detailed description of the PI transformation algorithm, a description of the different modes of addressing organization that can be used in PI memory, an analysis of the efficiency of parallel-hierarchical memory structures, and a discussion of the challenges and future research directions in the field of PI memory.

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“…The Anchors Hierarchy [23] is a data structure and algorithm for localizing data based on a triangle-inequality-obeying distance metric, and it is extended in [22] for parallel text clustering. In [1], a parallel text clustering algorithm based on the agglomerative hierarchical approach and message-passing is proposed.…”

Section: Related Workmentioning

confidence: 99%

A parallel text document clustering algorithm based on neighbors

Luo

Chung

2015

Cluster Comput

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In this paper, we propose a new parallel algorithm for text document clustering based on the concept of neighbor (Guha et al. in Inf Syst 25(5):345-366, 2000). If two documents are similar enough, they are considered as neighbors of each other. The new algorithm is named parallel k-means based on neighbors (PKBN), and it is a parallel version of sequential k-means based on neighbors (SKBN) that we proposed in Luo et al. (Data Knowl Eng 68(11):1271-1288, 2009). PKBN fully exploits the data-parallelism of SKBN and adopts a new parallel pairgenerating method to build the neighbor matrix. Our new parallel pair-generating method causes less communication overhead between processors than existing methods. PKBN is designed for message-passing multiprocessor systems and is implemented on a cluster of Linux workstations to analyze its performance. Our experimental results on real-life data sets demonstrate that PKBN is very efficient and has good scalability with respect to the number of processors and the size of data set.

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A Novel Parallel Algorithm for Clustering Documents Based on the Hierarchical Agglomerative Approach

Cited by 6 publications

References 10 publications

Parallel N-Path Quantification Hierarchical K-Means Clustering Algorithm for Video Retrieval

Parallel N-Path Quantification Hierarchical K-Means Clustering Algorithm for Video Retrieval

Architektoniczne, Strukturalne I Funkcjonalne Cechy Równoległo-Hierarchicznej Organizacji Pamięci

A parallel text document clustering algorithm based on neighbors

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