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IEEE 5th Conference on Knowledge Based Engineering and Innovation (KBEI)

作者： Kiani, Rasoul Montazeri, Mojtaba Minaei-Bidgoli, Behrouz Iran Univ Sci & Technol Sch Comp Engn Tehran Iran

ISBN: (纸本)9781728108728

Outliers are data with anomalous behaviors to other datasets. There are three different types of outliers, namely point anomaly, collective anomaly, and conditional anomaly. Different density-, clustering-, distance-, and distribution-based methods are used to detect outliers. It is obvious that before testing detection algorithms, a dataset that encompasses different types of outliers is required. In this paper an intelligent clustering algorithm is presented to produce a dataset consisting of different outliers. The other important point in this paper is the probability of two uninvestigated types of collective data among datasets that the anomalies are called type I and II. Results show that the proposed algorithm is capable of producing a dataset including different types of outliers. This dataset can be used in all outlier detection techniques. In addition to detection of point anomalies, it can detect all collective anomalies.

关键词： outlier detection anomaly detection clustering algorithm dataset

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How many clusters exist? Answer via maximum clustering similarity implemented in R

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Biostatistics and Epidemiology 2019年第1期3卷 62-79页

作者： Albatineh, Ahmed N. Wilcox, Meredith L. Zogheib, Bashar Niewiadomska-Bugaj, Magdalena Department of Community Medicine and Behavioral Sciences Kuwait University Kuwait Florida International University Miami FL United States Department of Mathematics and Natural Sciences American University of Kuwait Kuwait Department of Statistics Western Michigan University Kalamazoo MI United States

Finding the number of clusters in a data set is considered as one of the fundamental problems in cluster analysis. This paper integrates maximum clustering similarity (MCS), for finding the optimal number of clusters, into R^© statistical software through the package MCSim. The similarity between the two clustering methods is calculated at the same number of clusters, using Rand [Objective criteria for the evaluation of clustering methods. J Am Stat Assoc. 1971;66:846–850.] and Jaccard [The distribution of the flora of the alpine zone. New Phytologist. 1912;11:37–50.] indices, corrected for chance agreement. The number of clusters at which the index attains its maximum with most frequency is a candidate for the optimal number of clusters. Unlike other criteria, MCS can be used with circular data. Seven clustering algorithms, existing in R^©, are implemented in MCSim. A graph of the number of clusters vs. clusters similarity using corrected similarity indices is produced. Values of the similarity indices and a clustering tree (dendrogram) are produced. Several examples including simulated, real, and circular data sets are presented to show how MCSim successfully works in practice. © 2019 International Biometric Society–Chinese Region.

关键词： Circular data clustering algorithm Correction for chance agreement Number of clusters Similarity index

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Research on Multi-layer Adaptive Intrusion Detection Based on clustering and Neural Network 14

Research on Multi-layer Adaptive Intrusion Detection Based o...

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14th International Conference on Computer Science and Education (ICCSE)

作者： Chen, Yingyue Xiamen Univ Technol Econ & Management Expt Ctr Xiamen Fujian Peoples R China

ISBN: (纸本)9781728118468

Aiming at the shortcomings of current intrusion detection, a SOM neural network method based on clustering and network level is proposed in this paper. Through the identification of easy-to-use network intrusion type, the network intrusion data is cleaned and analyzed in a targeted manner and the multi-layer adaptive network intrusion detection clustering model is established. Network intrusion detection clustering result based on this model is compared with result based on other methods. The results show that this model has higher accuracy and equilibrium and the validity and feasibility of the method is further verified.

关键词： network security clustering algorithm invasion identification SOM neural network

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Codebook Design based on Self-organizing Map clustering for Limited Feedback MIMO Systems

Codebook Design based on Self-organizing Map Clustering for ...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Jiang, Jing Lu, Danping Zhen, Li Xian Univ Posts & Telecommun Shaanxi Key Lab Informat Commun Network & Secur Xian 710121 Peoples R China

ISBN: (纸本)9781728107325

Codebook design is a core technology in limited feedback multiple-input multiple-output (MIMO) communication system. However, conventional codebook designs usually assume that the channel vectors obey a uniform distribution. Motivated by the excellent classification and analysis abilities of clustering algorithms, we propose a self-organizing map (SOM) clustering based codebook design method that can adaptively learn an arbitrary propagation environment. By utilizing the statistical distribution of cumulative channel samples to construct quantized codewords, the proposed method is able to update the codebook adaptively according to the instantaneous channel state information. Simulation results are consistent with theoretical analysis, and demonstrate that the proposed codebook design method can achieve a significant performance improvement in the achievable rate region compared with the conventional one, especially in non-uniform channel distribution scenarios.

关键词： Multiple-input multiple-output (MIMO) systems codebook design clustering algorithm self-organizing map (SOM) data-driven

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A Systematic Approach to Horizontal clustering Analysis on Embedded RSA Implementation 25

A Systematic Approach to Horizontal Clustering Analysis on E...

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25th IEEE International Conference on Parallel and Distributed Systems (IEEE ICPADS)

作者： Shi, Fanyu Wei, Jizeng Sun, Dazhi Guo, Wei Tianjin Univ Div Intelligence & Comp Tianjin Key Lab Adv Networking Tianjin Peoples R China

ISBN: (纸本)9781728125831

Side-channel attacks are a threat to cryptographic algorithms running on embedded devices. The exponent blinding is the main countermeasure which resists the classical forms of side-channel attacks. Horizontal side-channel analysis is an effective method to overcome this kind of countermeasures and recover the secret key through the analysis of a single trace. However, it is very difficult to exploit leakage from a single trace in practice, because the segment borders of crucial operation are hard to find and the single execution possibly contains a lot of noise in a real application environment. In this paper, a framework for systematic approach is presented to overcome these drawbacks. Firstly, we propose a method to use the Hilbert-Huang transform for the segment borders detection and the noise reduction. Then, an effective clustering method is used to recover the private key. The proposed horizontal clustering approach is applied to a protected implementation on embedded devices. The results show that the success rate of the key recovery could be up to 98%. Finally, we analyze the mechanism of leakage exploration.

关键词： Side-channel analysis horizontal attack Hilbert-Huang transform unsupervised learning modular exponentiation clustering algorithm

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Higher Education Units Assessment Based on Data Envelopment Analysis and clustering Techniques

Higher Education Units Assessment Based on Data Envelopment ...

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International Arab Conference on Information Technology (ACIT)

作者： Najadat, Hassan Althebyan, Qutaibah Al-Omary, Yasmin Jordan Univ Sci & Technol Dept Comp Informat Syst Irbid Jordan Al Ain Univ Al Ain U Arab Emirates Jordan Univ Sci & Technol Irbid Jordan Jordan Univ Sci & Technol Dept Comp Sci Irbid Jordan

ISBN: (纸本)9781728130101

Higher education plays a vital role in community development and provides indicators of national strength in all aspects of life. Moreover, it is able to achieve the desired economic growth. Therefore, measurement performance of decision-making units is a vital process in this issue. Higher education institutions are multi-input and output institutions and this type of institutions is difficult to be evaluated using traditional economic methods. This study aims to evaluate the efficiency and quality of higher education institutions based on Data Envelopment Analysis (DEA). Furthermore, we also aim to find solutions and proposals for universities with low efficiency values (or inefficient) by identifying weaknesses in the resources of these universities. The DEA assumes that all Decision Making Units (DMUs) are homogenous in their environments while the DEA process is not enough to compare universities' performances. So, our work proposes to use an unsupervised data mining technique such as k-means algorithm to group universities with similar characteristics. Then, the DEA is utilized for each cluster separately. The result shows a better improvements and a fair comparison of performance between universities.

关键词： Data Envelopment Analysis clustering algorithm Performance Evaluation decision making units Input Output Universities

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Design of Backward Collision Warning and Avoidance System when On-street Parking Using LiDAR

Design of Backward Collision Warning and Avoidance System wh...

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作者： Qizhang Shen Mechanical Engineering Department University of California

In the past decades,Collision Avoidance System has grown to a level more developed than it has ever been,actively and significantly improving the safety of passengers of ***,it only focused on the period of time when the vehicles are driving,and ignored the moment when passengers are most vulnerable – when they are getting off the car as their attention to the surrounding drops and are no longer protected by the structure of the ***,in this paper,an additional system is proposed to warn the passengers to open the doors and get off with caution,and even interfere to stop them getting off temporarily.

关键词： automobile engineering automotive safety collision avoidance door crashes laser radar clustering algorithm

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Supporting decision makers in search-based product line architecture design using clustering 43

Supporting decision makers in search-based product line arch...

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43rd IEEE-Computer-Society Annual International Computers, Software and Applications Conference (COMPSAC)

作者： Freire, Willian Marques Bindewald, Carlos Vinicius Amaral, Aline M. M. Miotto Colanzi, Thelma Elita Univ Estadual Maringa Informat Dept State Univ Maringa UEM Maringa Parana Brazil

ISBN: (纸本)9781728126074

The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line (SPL). PLA design can be formulated as an optimization problem with many factors. In this context, the MOA4PLA approach was proposed to optimize PLA design using search algorithms and metrics specific to the context. MOA4PLA treats the PLA design as a multi-objective optimization problem. At the end of the search process several PLA design alternatives are presented to the decision maker, difficulting the decision about which PLA alternative best fits with his/her needs. In this sense, this work proposes the usage of clustering algorithms to group PLAs design alternatives, according to their characteristics and assist the decision maker in the choice of one PLA design for the SPL. For this purpose, an empirical study was carried out, involving quantitative and qualitative experiments. Such an study integrated the K-Means++ and DBSCAN clustering algorithms in the MOA4PLA approach. The results of the experiments were promising, since an appropriate grouping of the solutions can be quantitatively observed, and also, qualitatively, the suitability of the solutions to the decision makers needs was verified.

关键词： product line architecture multi-objective optimization clustering algorithm K-Means plus DBSCAN

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Solving customer insurance coverage recommendation problem using a two-stage clustering-classification model

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2019年第1期14卷 9-19页

作者： Khalili-Damghani, Kaveh Abdi, Farshid Abolmakarem, Shaghayegh Islamic Azad Univ South Tehran Branch Dept Ind Engn Tehran Iran

Insurance coverage recommendation problem (ICRP) in which the most suitable coverage for customers is suggested is an essential issue for an insurance company. ICRP helps insurance companies to give suitable services to their customers. In ICRP, the insurance company tried to mine the features and records of data associated with the customers to suggest them the most economic and fitted insurance plan. The insurance companies have large databases which are considered as a proper infrastructure to analyze, model and predict the customer behavior. In this paper, a two-stage clustering-classification model is proposed to suggest suitable insurance coverage for customers. The first stage addresses a data pre-screening phase and clustering of customers based on the record of insurance coverage. Well-known clustering algorithms are employed. The superior clustering algorithm is selected based on Davies-Bouldin metric. In the second stage, several filter and wrapper methods are implemented to select proper features. The selected features are assumed as inputs of K-nearest neighbor classification algorithm. The proposed approach is applied in a real case study for clustering the customers and recommend insurance coverage. The results show that the model is capable of suggesting suitable insurance coverage based on customers' characteristics. [GRAPHICS] .

关键词： Knowledge engineering consumer behavior modeling data mining clustering algorithm classification algorithm insurance coverage recommendation

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Bio-inspired energy efficient clustering approach for wireless sensor networks

Bio-inspired energy efficient clustering approach for wirele...

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International Conference on Wireless Networks and Mobile Communications (WINCOM)

作者： Agbehadji, Israel Edem Millham, Richard C. Fong, Simon James Jung, Jason J. Bui, Khac-Hoai Nam Abayomi, Abdultaofeek Frimpong, Samuel Ofori Durban Univ Technol Dept Informat Technol ICT & Soc Res Grp Durban South Africa Univ Macau Dept Comp & Informat Sci ICT & Soc Res Grp Taipa Macau Peoples R China Chung Ang Univ Seoul South Korea Korea Inst Sci & Technol Informat Daejeon South Korea

ISBN: (纸本)9781728126258

In this paper, we proposed an approach to clustering based on bio-inspired behaviour and distributed energy efficient model. The motivation to propose this clustering approach is due to the challenge of performance in terms of finding an efficient way to send data packets to base stations and to maintain the lifetime performance of wireless sensor networks. The bio-inspired approach adopted the behaviour of a bird called Kestrel. This behaviour is expressed using mathematical formulation and then translated into an algorithm. The bio-inspired algorithm is combined with the distributed energy efficient model for clustering to ensure efficient energy optimization. The proposed clustering approach, referred to as DEEC-KSA, is evaluated through simulation and compared with benchmarked clustering algorithms. The result of simulation showed that the performance of DEEC-KSA is efficient among the comparative clustering algorithms for energy optimization in terms of stability period, network lifetime and network throughput. Additionally, the proposed DEEC-KSA has the optimal time (in seconds) to send packets to base station successfully.

关键词： Edge computing load balancing wireless sensor network clustering algorithm kestrel-based search algorithm heterogeneous environment internet of things (IoT) analytics

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