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Joint International Conference on Pervasive Computing and Web Society

作者： Fu, Xiufen Wang, Yaguang Ge, Yanna Chen, Peiwen Teng, Shaohua Guangdong Univ Technol Sch Comp Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9783319092652;9783319092645

Along with the rapid development of information age, more and more data can be obtained from the Internet, it is very difficult to get useful information and knowledge from these huge amounts of data. On the foundation of the existing algorithm based on dbscan, a new improved incremental dbscan clustering algorithm is proposed. Combining with cloud computing open source framework Hadoop, the improved algorithm use the programming model of MapReduce which can easy write distributed applications and simplify distributed programme to divide a huge amounts of data elements into chunks and distribute the chunks across the cluster and run the algorithm as a MapReduce job, in this way, this improved algorithm of data mining is integrated with framework Hadoop by the dbscan clustering algorithm. When data manipulation (add or delete) has occurred in the database, what we need to do is to mine the mutative data and merge the similar clusters, and ultimately form the final knowledge mining. Compared with single node server serial arithmetic and the overall mining, the time delay of data processing will be reduced. In the last part, the paper verified the effectiveness by experiments and data analysis.

关键词： Hadoop MapReduce dbscan algorithm Incremental Mining

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Anomaly Detection Based on Enhanced dbscan algorithm

Anomaly Detection Based on Enhanced DBScan Algorithm

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International Conference on Advanced in Control Engineering and Information Science (CEIS)

作者： Chen, Zhenguo Li, YongFei N China Inst Sci & Technol Dept Comp Beijing 101601 Peoples R China

As the Internet becomes more pervasive, it is very important that the security mechanisms of a system are designed so as to prevent unauthorized access to system resources and data. The paper proposes a clustering algorithm that exploits enhanced dbscan algorithm in anomaly detection. The algorithm that can be used for mass data processing turns into the hot research point of anomaly detection, to form normal behavior profile on the audit records and adjust the profile timely as the program behavior changed. The experimental result shows that the anomaly detecting based on enhanced dbscan algorithm can a higher detection rate and a low rate of false positives of DARPA data sets. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011]

关键词： dbscan algorithm Anomaly Detecting Intrusion Detection

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A Semi-supervised Fuzzy Clustering Approach via Modifications of the dbscan algorithm 10th

A Semi-supervised Fuzzy Clustering Approach via Modification...

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10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions (ICSCCW)

作者： Bedalli, Erind Mancellari, Enea Rada, Rexhep Epoka Univ Tirana Albania Univ Elbasan Elbasan Albania

ISBN: (纸本)9783030352493;9783030352486

In the data mining context, semi-supervised learning is applicable in circumstances where only a scarce amount of information on the intrinsic structure of a dataset is available. This information may be in the form a few labelled instances or a relatively small set of constraints on the pairwise memberships of particular instances. In this study we are providing a semi-supervised fuzzy clustering model which modifies versions of conventional dbscan algorithm in order to generate soft clusters which foreclose the noise points. The employed modifications are mostly related to the control parameters of the algorithm intending to utilize the additional information (which in our case is in the form of a few labelled instances) and adaptations towards the fuzzy clustering approach. Finally, several experimental procedures have been conducted on synthetic and real-world benchmark datasets in order to assess the accuracy of our employed model and to compare it to the conventional algorithms of the respective domain.

关键词： Semi-supervised learning Density-based clustering dbscan algorithm Fuzzy clustering

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Explore Hot Spots of City Based on dbscan algorithm 4

Explore Hot Spots of City Based on DBSCAN Algorithm

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International Conference on Audio, Language and Image Processing

作者： Yu, Xiaoqing Ding, Yupu Wan, Wanggen Thuillier, Etienne Sch Commun & Informat Engn Shanghai Peoples R China Shanghai Univ Inst Smart City Shanghai Peoples R China Univ Technol Belfort Montbeliar Belfort France

ISBN: (纸本)9781479939039

Spatial clustering is one of the main methods of data mining and knowledge discovery. dbscan algorithm can be found in space with "noise" database clustering of arbitrary shape, is a kind of good clustering algorithm. This paper introduces the basic concept and principle of dbscan algorithm, and applies this algorithm to perform clustering analysis distributions of weibo location information. The article compare k-means algorithm with dbscan algorithm in order to prove the effectiveness of dbscan algorithm.

关键词： socialnetwork dbscan algorithm data mining k-means algorithm

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Evaluation Method of Switchgear State Based on Adaptive dbscan algorithm 5

Evaluation Method of Switchgear State Based on Adaptive DBSC...

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5th Asia Conference on Power and Electrical Engineering (ACPEE)

作者： Li, Yangyang Hu, Jinlei Lai, Junju Wang, Wei Yang, Fan Huang, He Guangdong Power Grid Corp Qingyuan Power Supply Bur Qingyuan Peoples R China Shanghai Univ Elect Power Sch Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781728152813

In order to better identify the outlier in the detection data and reasonably evaluate the state of the switchgear, we propose a method for evaluating the state of switchgear based on the Density-Based Spatial Clustering of Applications with Noise (dbscan). First, the feature quantities of the switchgear is composed of the insulation state index, the Transient Earth Voltage (TEV), the Ultrasonic Testing (UT), the ambient temperature, the ambient humidity and the operation life. Standardization method is carried out for all feature quantities to form multi-dimensional feature *** the k-average nearest neighbor method and mathematical expectation method are used to generate the R radius and the minimum number of MinPts parameters. The density threshold (Den) is introduced to automatically find the stable range of cluster number. Finally, In this paper, the field test data is used as an example to verify the feasibility of the method and provide a theoretical basis for the evaluation of switchgear.

关键词： switchgear state evaluation adaptive optimization dbscan algorithm outlier

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An Improved dbscan algorithm for Adaptively Determining Parameters in Multi-density Environment 2021

An Improved DBSCAN Algorithm for Adaptively Determining Para...

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2nd International Conference on Artificial Intelligence and Information Systems (ICAIIS )

作者： Chen, Feiya Beijing Univ Technol Sch Software Engn Beijing Peoples R China

ISBN: (纸本)9781450390200

The density-based spatial clustering dbscan algorithm can realize clustering of arbitrary shapes in noisy data sets. The dbscan algorithm needs to manually determine the Eps and Min Pts parameters. As a result, the accuracy of the clustering results directly depends on the user's choice of parameters. In the face of large-scale irregular data, fixed parameters lead to ineffective multi-density data clustering. ideal. In response to the above situation, an improved dbscan algorithm for adaptively determining parameters in a multi-density environment is proposed. The non-parametric kernel density estimation theory is used to analyze the distribution characteristics of data samples to automatically determine the Eps parameters, preprocess the data, and identify each Based on the density around the data object, the density threshold MinPts suitable for the density of the area is automatically generated. The manual intervention of the clustering process is avoided, and the automation of the clustering process is realized. This method can select reasonable Eps and MinPts parameters, and obtains clustering results with higher accuracy.

关键词： dbscan algorithm multi-density kernel density estimation adaptive

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A fast dbscan algorithm for big data based on efficient density calculation

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 203卷

作者： Hanafi, Nooshin Saadatfar, Hamid Univ Birjand Comp Engn Dept Birjand Iran

Today, data is being generated with a high speed. Managing large volume of data has become a challenge in the current age. Clustering is a method to analyze data that is generated in the Internet. Various approaches have been presented for data clustering until now. Among them, dbscan is a most well-known density-based clustering algorithm. This algorithm can detect clusters of different shapes and does not require prior knowledge about the number of clusters. A major part of the dbscan run-time is spent to calculate the distance of data from each other to find the neighbors of each sample in the dataset. The time complexity of this algorithm is O(n2);Therefore, it is not suitable for processing big *** this paper, dbscan is improved so that it can be applied to big datasets. The proposed method calculates accurately each sample density based on a reduced set of data. This reduced set is called the operational set. This collection is updated periodically. The use of local samples to calculate the density has greatly reduced the computational cost of clustering. The empirical results on various datasets of different sizes and dimensions show that the proposed algorithm increases the clustering speed compared to recent related works while having similar accuracy as the original dbscan algorithm.

关键词： Data Mining Clustering Big Data dbscan algorithm

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AT_dbscan: dbscan algorithm with Self-Variable Threshold

International Core Journal of Engineering

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International Core Journal of Engineering 2021年第4期7卷 409-417页

作者： Haifeng Ma Aihua Wu

Based on the analysis of the shortcomings of the density-based clustering algorithm dbscan and its improved algorithm, an improved algorithm of dbscan based on self-variation of threshold value is proposed: AT_dbscan algorithm. The algorithm calculates the distribution characteristics of the shortest neighbor distance of each node in the data set, and then calculates the distribution of different densities in the data set according to the distribution characteristics, and finally determines the different radius thresholds of different density distribution data areas based on this. In this way, different radius thresholds ε can be used for data in different areas to accept dbscan algorithm clustering. On the whole, it basically solves the unreasonable clustering results caused by the unfavorable selection of the parameter value ε at the initial stage of the dbscan algorithm, and the unreasonable clustering of data with different density distributions. The experimental results show that the algorithm has a good clustering effect, and the algorithm first obtains the radius threshold ε without manual setting, which is of great significance.

关键词： Data Mining kd-tree Clustering dbscan algorithm

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Anomaly Detection Based on Enhanced dbscan algorithm

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Procedia Engineering 2011年 15卷 178-182页

作者： Zhenguo Chen Yong Fei Li Department of Computer North China Institute of Science and Technology East Yanjiao Beijing 101601 China

关键词： dbscan algorithm Anomaly Detecting Intrusion Detection

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Restricted Randomness dbscan : A faster dbscan algorithm 21

Restricted Randomness DBSCAN : A faster DBSCAN Algorithm

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2021 Thirteenth International Conference on Contemporary Computing (IC3-2021)

作者： Sashakt Pathak Arushi Agarwal Ankita Ankita Mahendra Kumar Gurve Jaypee Institute of Information Technology India

ISBN: (纸本)9781450389204

Data Mining is the process of extracting useful and accurate information or patterns from large databases using different algorithms and methods of machine learning. To analyze the data, Clustering is one of the methods in which similar data is grouped together and dbscan clustering algorithm is the one, which is broadly used in numerous practical applications. This paper presents a more efficient density based clustering algorithm, which has the ability to discover cluster faster than the existing dbscan algorithm. The efficiency is achieved by restricting the randomness of choosing points from the dataset. Our proposed algorithm named Restricted Randomness dbscan (RR dbscan) is compared with conventional dbscan algorithm over 9 datasets on the basis of Silhouette Coefficient, Time taken in formation of clusters and accuracy. The results show that RR dbscan performs better than traditional dbscan in terms of accuracy and time taken to form clusters.

关键词： dbscan algorithm Clustering Restricted randomness

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