An incremental density-based clustering framework using fuzzy local clustering

Laohakiat, Sirisup; Sa-ing, Vera

doi:10.1016/j.ins.2020.08.052

Cited by 45 publications

(19 citation statements)

References 24 publications

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“…A data instance in clustering is often referred to as an object. However, in real life, real data often appear as an object, which may not only be a description of a number or a group of numbers but also a collection of attribute descriptions [8].…”

Section: Density Clustering Process Designmentioning

confidence: 99%

Research on Fast Recommendation Algorithm of Library Personalized Information Based on Density Clustering

Shi

Zhu

2022

Security and Communication Networks

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In order to improve the accuracy and efficiency of library information recommendation, this paper proposes a fast recommendation algorithm for library personalized information based on density clustering. According to the analysis of the clustering principle, the algorithm achieves the clustering of library information by designing density interval function. Then, the collection priority of library personalized information is judged, and the library personalized information is recommended quickly by designing tags according to the library users’ preferences. Experimental results show that the recommendation accuracy and F value of the proposed algorithm are higher than those of the two traditional algorithms, and its coverage rate is higher and the mean absolute error is lower, indicating that the proposed algorithm effectively achieves the design expectation.

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Section: Density Clustering Process Designmentioning

confidence: 99%

Research on Fast Recommendation Algorithm of Library Personalized Information Based on Density Clustering

Shi

Zhu

2022

Security and Communication Networks

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“…Moreover, fuzzy clustering has been preferred to hard clustering, due to its capability to better represent changes in data, which is a critical factor for stream data [1]. Indeed, for this reason, several extensions of fuzzy clustering algorithms have been proposed for data stream [10,17,27].…”

Section: Methodsmentioning

confidence: 99%

Balancing Data Within Incremental Semi-supervised Fuzzy Clustering for Credit Card Fraud Detection

Casalino

Castellano

Marvulli

2021

Atlantis Studies in Uncertainty Modelling

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As the number of online financial transactions increases, the problem of credit card fraud detection has become quite urgent. Machine learning methods, including supervised and unsupervised approaches, have been proven to be effective to detect fraudulent activities. In our previous work presented at EUSFLAT2019 we proposed the use of an incremental semi-supervised fuzzy clustering that processes both labeled and unlabeled data as a stream to create a classification model for credit card fraud detection. However, we observed that the results of the method were affected by data unbalancement. Indeed credit card fraud data are highly imbalanced since the number of fraudulent activities is far less than the genuine ones. In this work, to deal with the high data unbalance, different resampling methods are investigated and their empirical comparison is reported.

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“…An algorithm called DenStream, which expands the DBSCAN algorithm by adding subclusters was proposed by Laohakiat and Sa‐Ing 17 . This algorithm is a sort of technique that is assisted by online support for offline clustering.…”

Section: Relevant Workmentioning

confidence: 99%

“…An algorithm called DenStream, which expands the DBSCAN algorithm by adding subclusters was proposed by Laohakiat and Sa-Ing. 17 This algorithm is a sort of technique that is assisted by online support for offline clustering. By detecting and adding dense neighborhoods to data objects, this algorithm progressively generates local clusters.…”

Section: Relevant Workmentioning

confidence: 99%

RAPSAMS: Robust affinity propagation clustering on static android malware stream

Katebi

Rezakhani

Joudaki

et al. 2022

Concurrency and Computation

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This article proposed RAPSAMS, extending affinity propagation (AP) clustering to be robust in malware steaming. We use AP, which has been suggested as an approach for clustering a set of samples that by passing messages in different clusters, represents malware stream clustering. Then, by generating and adding adversarial examples, a method has been proposed to attack this clustering algorithm and try to make a robust algorithm against the proposed attack. Malware clustering has become an active research area in mobile security, in which the knowledge is being exploited in large quantities of the generated malware stream. Different procedures have been used that apply clustering to the malware's static features. This article focuses on selecting the most appropriate examples as centers for Android malware clusters and tries to add some perturbation to fool the clustering algorithm. There are two important challenges in this regard: (i) How to find the best representations for clustering.(ii) How to manage extracted patterns that include important data flow characteristics with different distributions. Malware stream clustering poses many challenges to solve this problem, including dealing with malware that is imported online, being able to process malware quickly and incrementally, dealing appropriately with time constraints, and the way that can manage important features patterns of malware stream.To examine the adaptability of the proposed methods of defense and attack, analyses were performed. The proposed approaches are tested on a couple of malware Android dataset benchmarks, namely, Contagio, Genome, and Drebin. We use permission, API, and intent features of these datasets. The obtained results from false positive rate (FPR) recognize that the amounts of the known algorithm for clustering, AP, increases to more than 50% afterward an offense occurs in a number of instances (like: Genome dataset in permission and intent features). Furthermore, by applying the label flipping attack (LFA) the accuracy measures decrease lower than 85% in all instances. Also, the proposed defense method decreased the FPR value by less than 30%, and the accuracy increased by more than 91%, in all cases. Consequently, the AP clustering will be robust against LFAs.

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An incremental density-based clustering framework using fuzzy local clustering

Cited by 45 publications

References 24 publications

Research on Fast Recommendation Algorithm of Library Personalized Information Based on Density Clustering

Research on Fast Recommendation Algorithm of Library Personalized Information Based on Density Clustering

Balancing Data Within Incremental Semi-supervised Fuzzy Clustering for Credit Card Fraud Detection

RAPSAMS: Robust affinity propagation clustering on static android malware stream

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