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Multi-input address incremental clustering for the Bitcoin blockchain based on Petri net model analysis

Digital Communications and Networks 2022年第5期8卷 680-686页

作者： Fangchi Qin Yan Wu Fang Tao Lu Liu Leilei Shi Anthony J.Miller Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace School of Computer Science and Telecommunication EngineeringJiangsu UniversityChina Birmingham Business School University of BirminghamUK School of Informatics University of LeicesterUK

Bitcoin is a cryptocurrency based on *** historical Bitcoin transactions are stored in the Bitcoin blockchain,but Bitcoin owners are generally *** is the reason for Bitcoin's pseudo-anonymity,therefore it is often used for illegal *** addresses are related to Bitcoin users'*** Bitcoin addresses have the potential to be analyzed due to the behavior patterns of Bitcoin ***,existing Bitcoin analysis methods do not consider the fusion of new blocks'data,resulting in low efficiency of Bitcoin address *** order to address this problem,this paper proposes an incremental Bitcoin address cluster method to avoid re-clustering when new block data is ***,a heuristic Bitcoin address clustering algorithm is developed to improve clustering accuracy for the Bitcoin *** results show that the proposed method increases Bitcoin address cluster efficiency and accuracy.

关键词： Bitcoin Blockchain Petri net incremental clustering

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incremental clustering algorithm via crossentropy

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Journal of Systems Engineering and Electronics 2005年第4期16卷 781-786页

作者： Guan Tao Xu Jiucheng Feng Boqin State Key Laboratory of Intelligent Technology and Systems Dept. of Computer Science and Technology Tsinghua Univ. Beijing 100080 P. R. China Coll. of Computer and Information Technology Henan Normal Univ. Xinxiang 453007 P. R. China Sehcol of Electronics and Information Engineering Xi'an Jiaotong Univ. Xi'an 710049 P. R. China

A new incremental clustering method is presented, which partitions dynamic data sets by mapping data points in high dimension space into low dimension space based on （fuzzy） cross-entropy（CE）. This algorithm is divided into two parts： initial clustering process and incremental clustering process. The former calculates fuzzy cross-entropy or cross-entropy of one point relafive to others and a hierachical method based on cross-entropy is used for clustering static data sets. Moreover, it has the lower time complexity. The latter assigns new points to the suitable cluster by calculating membership of data point to existed centers based on the cross-entropy measure. Experimental compafisons show the proposed methood has lower time complexity than common methods in the large-scale data situations cr dynamic work environments.

关键词： incremental clustering (fuzzy)cross-entropy hierachical clustering.

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Turbopump Condition Monitoring Using incremental clustering and One-class Support Vector Machine

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Chinese Journal of Mechanical Engineering 2011年第3期24卷 474-479页

作者： HU Lei HU Niaoqing QIN Guojun GU Fengshou College of Mechatronics Engineering andAutomation National University of Defense Technology Changsha 410073 China University ofHuddersfield Huddersfield QueensgateHD13DH UK

Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine （LRE）.Because of lack of fault samples,a monitoring system cannot be trained on all possible condition *** it is important to differentiate abnormal or unknown patterns from normal pattern with novelty detection ***-class support vector machine （OCSVM） that has been commonly used for novelty detection cannot deal well with large scale *** order to model the normal pattern of the turbopump with OCSVM and so as to monitor the condition of the turbopump,a monitoring method that integrates OCSVM with incremental clustering is *** this method,the incremental clustering is used for sample reduction by extracting representative vectors from a large training *** representative vectors are supposed to distribute uniformly in the object region and fulfill the *** training OCSVM on these representative vectors yields a novelty *** applying this method to the analysis of the turbopump＇s historical test data,it shows that the incremental clustering algorithm can extract 91 representative points from more than 36 000 training vectors,and the OCSVM detector trained on these 91 representative points can recognize spikes in vibration signals caused by different abnormal events such as vane shedding,rub-impact and sensor *** monitoring method does not need fault samples during training as classical recognition *** method resolves the learning problem of large samples and is an alternative method for condition monitoring of the LRE turbopump.

关键词： novelty detection condition monitoring incremental clustering one-class support vector machine turbopump

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New incremental clustering framework based on induction as inverted deduction

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Journal of Systems Engineering and Electronics 2009年第5期20卷 1132-1143页

作者： Lv Zonglei Wang Jiandong Xu Tao Coll. of Computer Science and Technology Civil Aviation Univ. of China Tianjin 300300 P. R. China Coll. of Information Science and Technology Nanjing Univ. of Aeronautics and Astronautics Nanjing 210016 P. R. China

A new incremental clustering framework is presented, the basis of which is the induction as inverted deduction. Induction is inherently risky because it is not truth-preserving. If the clustering is considered as an induction process, the key to build a valid clustering is to minimize the risk of clustering. From the viewpoint of modal logic, the clustering can be described as Kripke frames and Kripke models which are reflexive and symmetric. Based on the theory of modal logic, its properties can be described by system B in syntax. Thus, the risk of clustering can be calculated by the deduction relation of system B and proximity induction theorem described. Since the new proposed framework imposes no additional restrictive conditions of clustering algorithm, it is therefore a universal framework. An incremental clustering algorithm can be easily constructed by this framework from any given nonincremental clustering algorithm. The experiments show that the lower the a priori risk is, the more effective this framework is. It can be demonstrated that this framework is generally valid.

关键词： data mining clustering incremental clustering induction learning modal logic.

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incremental clustering for Categorical Data Using clustering Ensemble

Incremental Clustering for Categorical Data Using Clustering...

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第二十九届中国控制会议

作者： LI Taoying,CHNE Yan,QU Lili,MU Xiangwei Transportation Management College,Dalian Maritime University,Dalian 116026,P.R.China

More and more data in practice is changing every minute and been collected in incremental mode,and incremental clustering has attracted much of researchers’***,little research now focuses on partitioning categorical data in incremental *** to design incremental clustering for categorical data is an urgent *** propose an incremental clustering for categorical data using clustering ensemble in this *** firstly prune redundant attributes if needed,and then make use of true values of different attributes to form clustering memberships,and next use clustering ensemble to merge or divide clusters to gain optimal ***,the proposed algorithm is applied in Yellow-Small dataset,Diagnosis dataset and Zoo dataset and results show that it is effective.

关键词： Data Mining clustering incremental clustering clustering Ensemble

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Bat-Grey Wolf Optimizer and kernel mapping for automatic incremental clustering

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International Journal of Modeling, Simulation, and Scientific Computing 2020年第4期11卷 154-176页

作者： C.Vidyadhari N.Sandhya P.Premchand Computer Science and Engineering Department JNTUK KakinadaIndia Computer Science and Engineering Department VNRVJIET HyderabadIndia Computer Science and Engineering Department University College of Engineering Osmania University HyderabadIndia

The technical advancement in information systems contributes towards the massive availability of the documents stored in the electronic databases such as e-mails,internet and web ***,it becomes a complex task for arranging and browsing the required *** paper proposes an approach for incremental clustering using the BatGrey Wolf Optimizer(BAGWO).The input documents are initially subjected to the pre-processing module to obtain useful keywords,and then the feature extraction is performed based on wordnet *** feature extraction,feature selection is carried out using entropy ***,the clustering is done using the proposed BAGWO *** BAGWO algorithm is designed by integrating the Bat Algorithm(BA)and Grey Wolf Optimizer(GWO)for generating the different clusters of text ***,the clustering is determined using the BAGWO algorithm,yielding the group of *** the other side,upon the arrival of a new document,the same steps of pre-processing and feature extraction are *** on the features of the test document,the mapping is done between the features of the test document,and the clusters obtained by the proposed BAGWO *** mapping is performed using the kernel-based deep point distance and once the mapping terminated,the representatives are updated based on the fuzzy-based representative *** performance of the developed BAGWO outperformed the existing techniques in terms of clustering accuracy,Jaccard coefficient,and rand coefficient with maximal values 0.948,0.968,and 0.969,respectively.

关键词： incremental clustering Grey Wolf Optimizer bat optimization fuzzy concept kernel-based deep point distance.

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