scholarly journals Experimental comparison of the diagnostic capabilities of classification and clustering algorithms for the QoS management in an autonomic IoT platform

2019 ◽  
Vol 13 (3) ◽  
pp. 199-219 ◽  
Author(s):  
Luis Morales ◽  
Clovis Anicet Ouedraogo ◽  
Jose Aguilar ◽  
Christophe Chassot ◽  
Samir Medjiah ◽  
...  
Keyword(s):  
Clustering Algorithms ◽  
Experimental Comparison ◽  
Qos Management ◽  
Iot Platform ◽  
Diagnostic Capabilities ◽  
Classification And Clustering

scholarly journals Clustering Approaches for Pragmatic Two-Layer IoT Architecture

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
J. Sathish Kumar ◽  
Mukesh A. Zaveri
Keyword(s):  
Internet Of Things ◽  
Network Lifetime ◽  
Real World ◽  
Clustering Algorithms ◽  
Business And Society ◽  
Common People ◽  
Iot Platform ◽  
Efficient Communication ◽  
Architectural Framework ◽  
Real Time Application

Connecting all devices through Internet is now practical due to Internet of Things. IoT assures numerous applications in everyday life of common people, government bodies, business, and society as a whole. Collaboration among the devices in IoT to bring various applications in the real world is a challenging task. In this context, we introduce an application-based two-layer architectural framework for IoT which consists of sensing layer and IoT layer. For any real-time application, sensing devices play an important role. Both these layers are required for accomplishing IoT-based applications. The success of any IoT-based application relies on efficient communication and utilization of the devices and data acquired by the devices at both layers. The grouping of these devices helps to achieve the same, which leads to formation of cluster of devices at various levels. The clustering helps not only in collaboration but also in prolonging overall network lifetime. In this paper, we propose two clustering algorithms based on heuristic and graph, respectively. The proposed clustering approaches are evaluated on IoT platform using standard parameters and compared with different approaches reported in literature.

A New Similarity Metric for Sequential Data

2010 ◽  
Vol 6 (4) ◽  
pp. 16-32 ◽  
Author(s):  
Pradeep Kumar ◽  
Bapi S. Raju ◽  
P. Radha Krishna
Keyword(s):  
Data Mining ◽  
Similarity Measure ◽  
Web Mining ◽  
Clustering Algorithms ◽  
Sequential Data ◽  
Similarity Metric ◽  
Benchmark Datasets ◽  
Similarity Preserving ◽  
Sequential Nature ◽  
Classification And Clustering

In many data mining applications, both classification and clustering algorithms require a distance/similarity measure. The central problem in similarity based clustering/classification comprising sequential data is deciding an appropriate similarity metric. The existing metrics like Euclidean, Jaccard, Cosine, and so forth do not exploit the sequential nature of data explicitly. In this paper, the authors propose a similarity preserving function called Sequence and Set Similarity Measure (S3M) that captures both the order of occurrence of items in sequences and the constituent items of sequences. The authors demonstrate the usefulness of the proposed measure for classification and clustering tasks. Experiments were conducted on benchmark datasets, that is, DARPA’98 and msnbc, for classification task in intrusion detection and clustering task in web mining domains. Results show the usefulness of the proposed measure.

Urban green economic development indicators based on spatial clustering algorithm and blockchain

2020 ◽  
pp. 1-12
Author(s):  
Xiaoguang Gao
Keyword(s):  
Development Strategy ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
High Dimensional Data ◽  
Large Data ◽  
Experimental Comparison ◽  
High Dimensional ◽  
Density Peak ◽  
Data Set

The unbalanced development strategy makes the regional development unbalanced. Therefore, in the development process, resources must be effectively utilized according to the level and characteristics of each region. Considering the resource and environmental constraints, this paper measures and analyzes China’s green economic efficiency and green total factor productivity. Moreover, by expounding the characteristics of high-dimensional data, this paper points out the problems of traditional clustering algorithms in high-dimensional data clustering. This paper proposes a density peak clustering algorithm based on sampling and residual squares, which is suitable for high-dimensional large data sets. The algorithm finds abnormal points and boundary points by identifying halo points, and finally determines clusters. In addition, from the experimental comparison on the data set, it can be seen that the improved algorithm is better than the DPC algorithm in both time complexity and clustering results. Finally, this article analyzes data based on actual cases. The research results show that the method proposed in this paper is effective.

scholarly journals Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape

2021 ◽  
Vol 2 (2) ◽  
pp. 1-26
Author(s):  
Tommaso Zoppi ◽  
Andrea Ceccarelli ◽  
Tommaso Capecchi ◽  
Andrea Bondavalli
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Binary Classification ◽  
Clustering Algorithms ◽  
Good Alternative ◽  
Experimental Comparison ◽  
Support Vector ◽  
Self Organizing Maps ◽  
Wide Range ◽  
The Individual

Anomaly detection aims at identifying unexpected fluctuations in the expected behavior of a given system. It is acknowledged as a reliable answer to the identification of zero-day attacks to such extent, several ML algorithms that suit for binary classification have been proposed throughout years. However, the experimental comparison of a wide pool of unsupervised algorithms for anomaly-based intrusion detection against a comprehensive set of attacks datasets was not investigated yet. To fill such gap, we exercise 17 unsupervised anomaly detection algorithms on 11 attack datasets. Results allow elaborating on a wide range of arguments, from the behavior of the individual algorithm to the suitability of the datasets to anomaly detection. We conclude that algorithms as Isolation Forests, One-Class Support Vector Machines, and Self-Organizing Maps are more effective than their counterparts for intrusion detection, while clustering algorithms represent a good alternative due to their low computational complexity. Further, we detail how attacks with unstable, distributed, or non-repeatable behavior such as Fuzzing, Worms, and Botnets are more difficult to detect. Ultimately, we digress on capabilities of algorithms in detecting anomalies generated by a wide pool of unknown attacks, showing that achieved metric scores do not vary with respect to identifying single attacks.

SMART SWITCH INTELLIGENCE USING CLASSIFICATION AND CLUSTERING ALGORITHMS

2020 ◽  
Vol 76 (3) ◽  
Author(s):  
M. Ramasubramanian ◽  
A. Gauthami Latha
Keyword(s):  
Clustering Algorithms ◽  
Classification And Clustering
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