scholarly journals Mobile Anomaly Detection Based on Improved Self-Organizing Maps

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Chunyong Yin ◽  
Sun Zhang ◽  
Kwang-jun Kim
Keyword(s):  
Anomaly Detection ◽  
Mobile Devices ◽  
Clustering Algorithms ◽  
Recall Rate ◽  
Accuracy Rate ◽  
Optimal Method ◽  
Self Organizing Maps ◽  
Different Characteristics ◽  
Precision Rate ◽  
Self Organizing

Anomaly detection has always been the focus of researchers and especially, the developments of mobile devices raise new challenges of anomaly detection. For example, mobile devices can keep connection with Internet and they are rarely turned off even at night. This means mobile devices can attack nodes or be attacked at night without being perceived by users and they have different characteristics from Internet behaviors. The introduction of data mining has made leaps forward in this field. Self-organizing maps, one of famous clustering algorithms, are affected by initial weight vectors and the clustering result is unstable. The optimal method of selecting initial clustering centers is transplanted from K-means to SOM. To evaluate the performance of improved SOM, we utilize diverse datasets and KDD Cup99 dataset to compare it with traditional one. The experimental results show that improved SOM can get higher accuracy rate for universal datasets. As for KDD Cup99 dataset, it achieves higher recall rate and precision rate.

scholarly journals Implementation of Simple Additive Weighting (SAW) Method and Profile Matching for Employee Selection

2021 ◽  
Vol 10 (01) ◽  
pp. 022-029
Author(s):  
Andi Pratomo Wiyono ◽  
Muhammad Aziz Muslim ◽  
Muhammad Aswin
Keyword(s):  
Selection Process ◽  
Confusion Matrix ◽  
Employee Selection ◽  
Recall Rate ◽  
Accuracy Rate ◽  
Profile Matching ◽  
Simple Additive Weighting ◽  
A Company ◽  
Precision Rate ◽  
F Measure

Employees are an important element in a company that determines the progress of a company. With good quality employees in a company, it is easier to achieve desired goals of a company. Conventional (manual) recruitment method is vulnerable to non-technical factors such as frequent duplicate data or invalid data. In such condition, a Decision Support System (DSS) will be helpful in making decision process valid and reliable. In this paper, a Simple Addictive Weighting (SAW) method and Profile Matching were proposed to solve employee selection problem. This research was conducted at UPT Career Development and Entrepreneurship Universitas Brawijaya Malang, using data collected from written test selection in 2019. The effectiveness of both methods is analyzed by means of confusion matrix. SAW method give Accuracy rate of 94.7%, Precision rate of 87.5%, Recall rate of 91.3% and F-measure rate of 89.4%. On the other hand, Profile Matching method obtained the Accuracy rate of 90.4.7%, Precision rate of 81.4%, Recall rate of 81.4% and F-measure rate of 81.4%. From these results, it can be concluded that both methods have a high accuracy value accompanied by a high precision value when used for the selection process. This system can also reduce the bias of the same data very well, as can be seen from the high Recall and F-measure rates.

scholarly journals Novel Algorithms for Graph Clustering Applied to Human Activities

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1089
Author(s):  
Nebojsa Budimirovic ◽  
Nebojsa Bacanin
Keyword(s):  
Human Activities ◽  
Clustering Algorithms ◽  
Weighted Graph ◽  
Graph Clustering ◽  
Recall Rate ◽  
Number Of Clusters ◽  
Starting Point ◽  
Measurement Units ◽  
Novel Algorithms ◽  
Precision Rate

In this paper, a novel algorithm (IBC1) for graph clustering with no prior assumption of the number of clusters is introduced. Furthermore, an additional algorithm (IBC2) for graph clustering when the number of clusters is given beforehand is presented. Additionally, a new measure of evaluation of clustering results is given—the accuracy of formed clusters (T). For the purpose of clustering human activities, the procedure of forming string sequences are presented. String symbols are gained by modeling spatiotemporal signals obtained from inertial measurement units. String sequences provided a starting point for forming the complete weighted graph. Using this graph, the proposed algorithms, as well as other well-known clustering algorithms, are tested. The best results are obtained using novel IBC2 algorithm: T = 96.43%, Rand Index (RI) 0.966, precision rate (P) 0.918, recall rate (R) 0.929 and balanced F-measure (F) 0.923.

scholarly journals ELECTROFACIES CLASSIFICATION OF PONTA GROSSA FORMATION BY MULTI-RESOLUTION GRAPH-BASED CLUSTERING (MRGC) AND SELF-ORGANIZING MAPS (SOM) METHODS

2020 ◽  
Vol 38 (1) ◽  
pp. 52
Author(s):  
Felipe Vasconcelos dos Passos ◽  
Marco Antonio Braga ◽  
Thiago Gonçalves Carelli ◽  
Josiane Branco Plantz
Keyword(s):  
Gamma Ray ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Self Organizing Maps ◽  
Resolution Graph ◽  
Lithology Prediction ◽  
Paraná Basin ◽  
Parana Basin ◽  
Graph Based Clustering ◽  
Self Organizing

ABSTRACT. In Ponta Grossa Formation, devonian interval of Paraná Basin, Brazil, sampling restrictions are frequent, and lithological interpretations from gamma ray logs are common. However, no single log can discriminate lithology unambiguously. An alternative to reduce the uncertainty of these assessments is to perform multivariate analysis of well logs using data clustering methods. In this sense, this study aims to apply two different clustering algorithms, trained with gamma ray, sonic and resistivity logs. Five electrofacies were differentiated and validated by core data. It was found that one of the electrofacies identified by the model was not distinguished by macroscopic descriptions. However, the model developed is sufficiently accurate for lithological predictions.Keywords: geophysical well logging, lithology prediction, Paraná Basin. CLASSIFICAÇÃO DE ELETROFÁCIES DA FORMAÇÃO PONTA GROSSA UTILIZANDO OS MÉTODOS MULTI-RESOLUTION GRAPH-BASED CLUSTERING (MRGC) E SELF-ORGANIZING MAPS (SOM)RESUMO. Na Formação Ponta Grossa, intervalo devoniano da Bacia do Paraná, Brasil, restrições de amostragem são frequentes e interpretações litológicas dos registros de raios gama são comuns. No entanto, nenhum perfil geofísico único pode discriminar litologias sem ambiguidade. Uma alternativa para reduzir a incerteza dessas avaliações é executar uma análise multivariada combinando vários perfis geofísicos de poços por meio de métodos de agrupamento de dados. Nesse sentido, este estudo tem como objetivo aplicar dois algoritmos de agrupamento aos registros de raios gama, sônico e resistividade para fins de predição litológica. Cinco eletrofácies foram diferenciadas e validadas por dados de testemunhos. Verificou-se que uma classe identificada pelo modelo não foi identificada por descrições macroscópicas. Porém, o modelo é suficientemente preciso para predições litológicas.Palavras-chave: geofísica de poços, predição litológica, correlação rocha-perfil, Bacia do Paraná.

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