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7th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR)

作者： Hou, Jian Liu, Weixue Xu E Bohai Univ Coll Engn Jinzhou 121013 Peoples R China Univ Ca Foscari Venezia ECLT I-30124 Venice Italy Bohai Univ Coll Informat Sci Jinzhou 121013 Peoples R China

ISBN: (纸本)9783319461823;9783319461816

While density based clustering algorithms are able to detect clusters of arbitrary shapes, their clustering results usually rely heavily on some user-specified parameters. In order to solve this problem, in this paper we propose to combine dominant sets and density based clustering to obtain reliable clustering results. We firstly use the dominant sets algorithm and histogram equalization to generate the initial clusters, which are usually subsets of the real clusters. In the second step the initial clusters are extended with density based clustering algorithms, where the required parameters are determined based on the initial clusters. By merging the merits of both algorithms, our approach is able to generate clusters of arbitrary shapes without user-specified parameters as input. In experiments our algorithm is shown to perform better than or comparably to some state-of-the-art algorithms with careful parameter tuning.

关键词： Dominant set Histogram equalization density based clustering Cluster extension

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density based clustering for Cricket World Cup tweets using Cosine similarity and time parameter 12

Density based clustering for Cricket World Cup tweets using ...

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12 IEEE Int C Elect Energy Env Communications Computer Control

作者： Pandey, Nilang LJ Inst Engn & Technol PG CE Dept Ahmadabad Gujarat India

ISBN: (纸本)9781467365406

The rapid spread of location-based devices and cheap storage mechanisms, as well as fast development of Internet technology, allowed collection and distribution of huge amounts of user-generated data. These user generated data sometimes are known as georeferenced documents, they have their location information and time of posting embedded with them. These parameters help to retrieve the location information and the time of posting. We need to retrieve the topic from those geo-referenced documents and determine the local topics and events for a particular region. All these clusters are geospatial in arbitrary shape hence density based clustering is the most appropriate clustering algorithm. Here we used tweets from Twitter, while the DBSCAN method is used for generating clusters. Here for finding similarity between tweets cosine similarity is used, but because of its low value we increase its value by adding weight to it by matching the keywords in tweets. Also another parameter of time is used for separating clusters temporally. Results have shown that weighted keyword based method gives more specific clusters than DBSCAN method, while using the time parameter in it we get clusters time separated. Hence for purpose of information retrieval or building marketing strategy by tweets we can use this method.

关键词： density based clustering geo-referenced documents spatio-temporal clusters spatial clusters topic retrieval location detection

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A survey of density based clustering algorithms

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Frontiers of Computer Science 2021年第1期15卷 139-165页

作者： Panthadeep BHATTACHARJEE Pinaki MITRA Department of Computer Science and Engineering Indian Institute of TechnologyGuwahati 781039India

density based clustering algorithms(DBCLAs)rely on the notion of density to identify clusters of arbitrary shapes,sizes with varying *** surveys on DB-CLAs cover only a selected set of *** surveys fail to provide an extensive information about a variety of DBCLAs proposed till date including a taxonomy of the *** this paper we present a comprehensive survey of various DB-CLAS over last two decades along with their *** group the DBCLAs in each of the four categories:density definition,parameter sensitivity,execution mode and nature of*data and further divide them into various classes under each of these *** addition,we compare the DBCLAs through their common features and variations in citation and conceptual *** identify various application areas of DBCLAS in domains such as astronomy,earth sciences,molecular biology,geography,*** survey also identifies probable future directions of DBCLAs where involvement of density based methods may lead to favorable results.

关键词： clustering density based clustering survey classification common properties applications

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Detecting protein complexes from DPINs by density based clustering with Pigeon-Inspired Optimization Algorithm

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Science China(Information Sciences) 2016年第7期59卷 30-43页

作者： Xiujuan LEI Yulian DING Fang-Xiang WU School of Computer Science Shaanxi Normal University School of Mathematical Sciences Nankai University Division of Biomedical Engineering University of Saskatchewan

Detecting protein complexes is crucial to understand principles of cellular organization. Plenty evidences have indicated that sub-graphs with high density in protein-protein interaction(PPI) network, especially dynamic PPI network(DPIN), usually correspond to protein complexes. As a well-known density-based clustering algorithm, density-based Spatial clustering of Applications with Noise(DBSCAN) has been used in many areas due to its simplicity and the ability to detect clusters of different sizes and shapes. However, one of its limitations is that the performance of DBSCAN depends on two specified parameters ε and Min Pts, where εrepresents the maximum radius of a neighborhood from an observing point while Min Pts means the minimum number of data points contained in such a neighborhood. In this article, we develop a new method named as P-DBSCAN to detect protein complexes in DPIN by using Pigeon-Inspired Optimization(PIO) Algorithm to optimize the parameters ε and Min Pts in DBSCAN. The experiments on DIP and MIPS datasets show that P-DBSCAN outperforms the state-of-the-art methods for protein complex detection in terms of several criteria such as precision, recall and f-measure.

关键词： dynamic protein-protein interaction network(DPIN) pigeon-inspired optimization(PIO) protein complex density based clustering gene expression

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Unsupervised varied density based clustering algorithm using spline

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PATTERN RECOGNITION LETTERS 2017年 93卷 48-57页

作者： Louhichi, Soumaya Gzara, Mariem Ben-Abdallah, Hanene MIRACL Multimedia InforRmat Syst & Adv Comp Lab BP 1030 Sfax 3018 Tunisia King Abdulaziz Univ Fac Comp & Informat Technol Jeddah Saudi Arabia

Building upon the promising performances of density-based clustering, we present a novel density-based clustering algorithm called MDCUT (Multidensity clustering). The presented algorithm has the merit of clustering data with varied density. It operates in two phases. First, it finds the appropriate number of density levels in a data set;to do so, it uses the exponential spline mathematical process on the k-nearest neighbors' distance. Secondly, it uses these levels as local density thresholds to determine clusters with varied densities and arbitrary shapes. We show experimentally that the MDCUT algorithm detects correctly the density levels in a data set and succeeds to discover arbitrarily shaped clusters in decreasing density order. We validate the clustering results in terms of clustering error, precision and recall rates on various data sets. MDCUT performs well in comparison to several other clustering algorithms among which the DBSCAN algorithm. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Data mining density based clustering Varied density clustering Interpolation Exponential spline

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State transition in communication under social network: An analysis using fuzzy logic and density based clustering towards big data paradigm

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2016年第0期65卷 207-220页

作者： Ghosh, Goldina Banerjee, Soumya Yen, Neil Y. Birla Inst Technol Dept Comp Sci & Engn Mesra India Univ Aizu Sch Comp Sci & Engn Aizu Wakamatsu Fukushima Japan

Social networks like Facebook, Twitter, Linkedln, are the social clouds that provide a platform for a diversified range of conversations on any theme at a stipulated time. The flow rate of a conversation can be measured from the perspective of different amalgamations formed by the theme and the level of the participation. Multiple users share the cloud resources and reallocation of the resources is also possible within the social network as and when required. The communication and conversation can be influential extensively due to the presence of big data orientation. It has been observed that depending on certain random parameters of big data driven social network few blocks of conversation may become explicit and influence on the arena of social network. The element of randomness and diversity creates ambiguity in the content of conversation. The conversation may become appealing or may lose the sympathy of the participants. This paper proposes a novel algorithm deploying Fuzzy methodology to investigate the embedded uncertainty and ambiguity involved within the conversation blocks. Fuzzy logic is capable of taking care of non-crisp states of conversation that might be the source of branding and putting a particular content to the maximum elevation across the social media and services. The experimental results and the graphs give the justification towards the effect of participants act on the topic discussion session, especially the periphery of big data framework. The pre-condition and the post-condition of the topic have been observed after implementing the proposed algorithm and finally further scope of the research has been discussed. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Social big data Participation-based scenario Human-centric computing Fuzzy logic Composite relation density based clustering

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Algorithm to determine ε-distance parameter in density based clustering

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第6期41卷 2939-2946页

作者： Jahirabadkar, Sunita Kulkarni, Parag Coll Engn Pune Maharashtra India

The well known clustering algorithm DBSCAN is founded on the density notion of clustering. However, the use of global density parameter epsilon-distance makes DBSCAN not suitable in varying density datasets. Also, guessing the value for the same is not straightforward. In this paper, we generalise this algorithm in two ways. First, adaptively determine the key input parameter epsilon-distance, which makes DBSCAN independent of domain knowledge satisfying the unsupervised notion of clustering. Second, the approach of deriving epsilon-distance based on checking the data distribution of each dimension makes the approach suitable for subspace clustering, which detects clusters enclosed in various subspaces of high dimensional data. Experimental results illustrate that our approach can efficiently find out the clusters of varying sizes, shapes as well as varying densities. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Data mining clustering density based clustering Subspace clustering High dimensional data

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Rough-DBSCAN: A fast hybrid density based clustering method for large data sets

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PATTERN RECOGNITION LETTERS 2009年第16期30卷 1477-1488页

作者： Viswanath, P. Babu, V. Suresh Univ Bedfordshire Inst Res Applicable Comp Dept Comp & Informat Syst Luton LU1 3JU Beds England NRI Inst Technol Pattern Recognit Res Lab Dept Comp Sci & Engn Guntur 522009 Andhra Pradesh India

density based clustering techniques like DBSCAN are attractive because it can find arbitrary shaped clusters along with noisy outliers. Its time requirement is O(n(2)) where n is the size of the dataset, and because of this it is not a suitable one to work with large datasets. A solution proposed in the paper is to apply the leaders clustering method first to derive the prototypes called leaders from the dataset which along with prototypes preserves the density information also, then to use these leaders to derive the density based clusters. The proposed hybrid clustering technique called rough-DBSCAN has a time complexity of O(n) only and is analyzed using rough set theory. Experimental studies are done using both synthetic and real world datasets to compare rough-DBSCAN with DBSCAN. It is shown that for large datasets rough-DBSCAN can find a similar clustering as found by the DBSCAN, but is consistently faster than DBSCAN. Also some properties of the leaders as prototypes are formally established. (C) 2009 Elsevier B.V. All rights reserved.

关键词： clustering density based clustering DBSCAN Leaders Rough sets

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Efficient implementation and parallelization of fuzzy density based clustering

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INFORMATION SCIENCES 2021年 575卷 454-467页

作者： Atilgan, Can Tezel, Baris Tekin Nasiboglu, Efendi Dokuz Eylul Univ Dept Comp Sci Izmir Turkey Inst Control Syst ANAS Baku Azerbaijan

clustering is a commonly used tool for data management and analysis. One of the prominent group of clustering methods consists of the density-based clustering algo-rithms. The use of fuzzy neighborhood functions for density-based clustering algorithms are known to significantly improve the robustness, such that choosing neighborhood parameters is rather easy for the user. On the other hand, because of the overhead of the fuzzy calculations, they demand higher computing resources. This study discusses how FN-DNSCAN-a fuzzy density-based clustering algorithm-can be implemented efficiently. A rather specific FN-DBSCAN algorithm that adopts techniques used to improve classical density-based clustering algorithms is introduced. Also, a parallel version of the algorithm is proposed and their implementation details are discussed. The proposed algorithms are tested in a set of comparative experiments, along with a straightforward FN-DBSCAN implementation and a curious but unsafe modification of the parallel algorithm. The results of the experiments that are conducted in a modest parallel computing environment of 32 processing units, show a wide variety of differences in relative speed-ups ranging from 2 to 850 times. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Fuzzy neighborhood Fuzzy clustering density based clustering FN-DBSCAN High performance computing

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A New density based clustering Algorithm for Binary Data Sets

A New Density Based Clustering Algorithm for Binary Data Set...

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International Conference on High Performance Computing and Applications (ICHPCA)

作者： Nanda, Satyasai Jagannath Raman, Rahul Vijay, Shubham Bhardwaj, Anil Malaviya Natl Inst Technol Dept Elect & Commun Engn Jaipur Rajasthan India Shri Mata Vaishno Devi Univ Sch Elect & Commun Engn Katra India

ISBN: (纸本)9781479959587

Binary Data clustering finds tremendous applications in fault analysis of machineries, document classification, image retrievals and analysis, medical diagnosis of diseases etc. Accurate clustering of binary databases provides numerous help to develop accurate designs of above systems. In this manuscript a density based clustering algorithm is proposed to effectively cluster binary datasets. The proposed algorithm automatically determines the number of clusters based upon the density of data present in a region. The number of clusters evolve during the clustering process due to merging of several smaller clusters. Simulation studies were carried out on two synthetic datasets and it is observed that the proposed algorithm can effectively clusters both correlated and random binary datasets.

关键词： density based clustering DBSCAN Binary Datasets Similarity Measure

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