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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 densities.Existing surveys on DB-CLAs cover only a selected set of algorithms.These surveys fail to provide an extensive information about a variety of DBCLAs proposed till date including a taxonomy of the algorithms.In this paper we present a comprehensive survey of various DB-CLAS over last two decades along with their classification.We 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 categories.In addition,we compare the DBCLAs through their common features and variations in citation and conceptual dependencies.We identify various application areas of DBCLAS in domains such as astronomy,earth sciences,molecular biology,geography,multimedia.Our 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

A Novel Method of Deinterleaving Radar Pulse Sequences based on a Modified DBSCAN Algorithm

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China Communications 2023年第2期20卷 198-215页

作者： Abolfazl Dadgarnia Mohammad Taghi Sadeghi Department of Electrical Engineering Yazd UniversityYazdIran

A modified DBSCAN algorithm is presented for deinterleaving of radar pulses in modern EW environments.A main characteristic of the proposed method is that using only time of arrival of pulses,the method can sort the pulses efficiently.Other PDW information such as rise time,carrier frequency,pulse width,modulation on pulse,fall time and direction of arrival are not required.To identify the valid PRIs in a set of interleaved pulses,an innovative modification of the DBSCAN algorithm is introduced which is accurate and easy to implement.The proposed method determines valid PRIs more accurately and neglects the spurious ones more efficiently as compared to the classical histogram based algorithms such as SDIF.Furthermore,without specifying any input parameter,the proposed method can deinterleave radar pulses while up to 30%jitter is present in the associated PRI.The accuracy and efficiency of the proposed method are verified by computer simulations and real data results.Experimental simulations are based on different real and operational scenarios where the presence of missing and spurious pulses are also considered.So,the simulation results can be of practical significance.

关键词： deinterleaving radar pulse sequences density based clustering pulse descriptor word

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Identification and characterization of irregular consumptions of load data

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Journal of Modern Power Systems and Clean Energy 2017年第3期5卷 465-477页

作者： Desh Deepak SHARMA S.N.SINGH Jeremy LIN Elham FORUZAN Indian Institute of Technology KanpurKanpurIndia PJM Interconnection AudubonPAUSA Department of Electrical Engineering University of Nebraska–LincolnLincolnNEUSA

The historical information of loadings on substation helps in evaluation of size of photovoltaic(PV)generation and energy storages for peak shaving and distribution system upgrade deferral. A method, based on consumption data, is proposed to separate the unusual consumption and to form the clusters of similar regular consumption. The method does optimal partition of the load pattern data into core points and border points, high and less dense regions, respectively. The local outlier factor, which does not require fixed probability distribution of data and statistical measures, ranks the unusual consumptions on only the border points, which are a few percent of the complete data. The suggested method finds the optimal or close to optimal number of clusters of similar shape of load patterns to detect regular peak and valley load demands on different days. Furthermore,identification and characterization of features pertaining to unusual consumptions in load pattern data have been done on border points only. The effectiveness of the proposed method and characterization is tested on two practical distribution systems.

关键词： density based clustering Irregular consumption Local outlier factor Peak demand Valley demand

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An Effective density based Approach to Detect Complex Data Clusters Using Notion of Neighborhood Difference

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International Journal of Automation and computing 2017年第1期14卷 57-67页

作者： S. Nagaraju Manish Kashyap Mahua Bhattachraya Visual Information Processing Lab Indian Institute of Information Technology and Management Gwalior India

The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.

关键词： density based clustering neighborhood difference density-based spatial clustering of applications with noise （DBSCAN）,space density indexing （SDI） core object.

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