Clustering Techniques and the Similarity Measures used in Clustering: A Survey

Irani, J.R.; Pise, Nitin; Phatak, Madhura

doi:10.5120/ijca2016907841

Cited by 83 publications

(42 citation statements)

References 24 publications

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“…The process then calculates centroid vectors, which represent the average weight of documents in the cluster, and measures similarity between two documents using measures like Cosine Similarity that calculates the angle between two vectors. [20] In our study, we employ two methods of Non-Hierarchal Clustering, which are K-Means and K-Medoids.…”

Section: ) Clusteringmentioning

confidence: 99%

Mining the Dark Web: A Novel Approach for Placing a Dark Website under Investigation

Alkhatib¹,

Basheer²

2019

IJMECS

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In the last two decades, illicit activities have dramatically increased on the Dark Web. Every year, Dark Web witnesses establishing new markets, in which administrators, vendors, and consumers aim to illegal acquisition and consumption. On the other hand, this rapid growth makes it quite difficult for law and security agencies to detect and investigate all those activities with manual analyses. In this paper, we introduce our approach of utilizing data mining techniques to produce useful patterns from a dark web market contents. We start from a brief description of the methodology on which the research stands, then we present the system modules that perform three basic missions: crawling and extracting the entire market data, data pre-processing, and data mining. The data mining methods include generating Association Rules from products' titles, and from the generated rules, we infer conceptual compositions vendors use when promoting their products. Clustering is the second mining aspect, where the system clusters vendors and products. From the generated clusters, we discuss the common characteristics among clustered objects, find the Top Vendors, and analyze products promoted by the latter, in addition to the most viewed and sold items on the market. Overall, this approach helps in placing a dark website under investigation.

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Section: ) Clusteringmentioning

confidence: 99%

Mining the Dark Web: A Novel Approach for Placing a Dark Website under Investigation

Alkhatib¹,

Basheer²

2019

IJMECS

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“…Generally, distance metrics play a very important role in order to measure the similarity among the data sets [ 24 ]. For the convenience of calculating the distance between any two SUs, the second task of the data manager is to extract each cooperating SU’s sensing data from the 0-1 database as a vector in the current CSS action.…”

Section: Collusive Ssdf Attack and Defense Schemementioning

confidence: 99%

Securing Cooperative Spectrum Sensing Against Collusive SSDF Attack using XOR Distance Analysis in Cognitive Radio Networks

Feng

Zhang

Xiao

et al. 2018

Sensors

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Cooperative spectrum sensing (CSS) is considered as a powerful approach to improve the utilization of scarce spectrum resources. However, if CSS assumes that all secondary users (SU) are honest, it may offer opportunities for attackers to conduct a spectrum sensing data falsification (SSDF) attack. To suppress such a threat, recent efforts have been made to develop trust mechanisms. Currently, some attackers can collude with each other to form a collusive clique, and thus not only increase the power of SSDF attack but also avoid the detection of a trust mechanism. Noting the duality of sensing data, we propose a defense scheme called XDA from the perspective of XOR distance analysis to suppress a collusive SSDF attack. In the XDA scheme, the XOR distance calculation in line with the type of “0” and “1” historical sensing data is used to measure the similarity between any two SUs. Noting that collusive SSDF attackers hold high trust value and the minimum XOR distance, the algorithm to detect collusive SSDF attackers is designed. Meanwhile, the XDA scheme can perfect the trust mechanism to correct collusive SSDF attackers’ trust value. Simulation results show that the XDA scheme can enhance the accuracy of trust evaluation, and thus successfully reduce the power of collusive SSDF attack against CSS.

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“…This simply means quick access of data, whenever the user demands the information. Clustering measure similarity between different groups [11]. An example of k-means clustering is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

An Extended Hybrid Clustering Method Utilzing Svm As Cross Validator

Sharma*,

Saini

2019

IJITEE

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The massive data accumulation from the internet creates attention for the researchers. The data collected in the form of structured and unstructured data. The structured data consists of messages, transactions, conversations, etc. while unstructured represents video and audio clips. This essentially manages the raw data problem in which unreferenced clustering is used. A hybrid approach is proposed using Cosine Similarity and soft cosine. A novel clustering technique is designed which is cross-validated using the Support Vector Machine (SVM). The validated approach is further verified by using K- means clustering. The clustering results have been further evaluated using parameters precision, recall, and F-measure. The evaluated results show the improvement in precision and recall accuracy due to hybridization of cosine similarity and soft cosine techniques

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Clustering Techniques and the Similarity Measures used in Clustering: A Survey

Cited by 83 publications

References 24 publications

Mining the Dark Web: A Novel Approach for Placing a Dark Website under Investigation

Mining the Dark Web: A Novel Approach for Placing a Dark Website under Investigation

Securing Cooperative Spectrum Sensing Against Collusive SSDF Attack using XOR Distance Analysis in Cognitive Radio Networks

An Extended Hybrid Clustering Method Utilzing Svm As Cross Validator

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