scholarly journals HIERARCHICAL CLUSTERING ALGORITHM FOR DETECTING ANOMALOUS PROFILES IN COMPUTER SYSTEMS

2014 ◽  
pp. 72-78
Author(s):  
Rachid Beghdad
Keyword(s):  
Intrusion Detection ◽  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Detection Method ◽  
Detection Methods ◽  
System A ◽  
Unix System ◽  
Mining Methods ◽  
Simulation Results ◽  
Hierarchical Clustering Algorithm

We introduce a new intrusion detection method based on the Hierarchical Clustering Algorithm (HCA), to detect anomalous user’s profiles. In the Unix system, a simple user has only some privileges (can access to some resources), but the root user has more privileges. So, we can speak here about hierarchy of users. By the same way, we can use a hierarchy of users in intrusion detection field, to distinguish between the normal user and suspicious user. Many data mining methods were already used in previous works in intrusion detection. Even if some of them led to interesting results, but they still suffer from some weaknesses. This is the reason why we focused in this study on the use of the HCA to detect anomalous profiles. A survey of intrusion detection methods is presented. The HCA procedure is described in detail. Our simulation results demonstrate the robustness of our approach in comparison to some previous used methods.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

scholarly journals Hesitant Fuzzy Linguistic Agglomerative Hierarchical Clustering Algorithm and Its Application in Judicial Practice

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 370
Author(s):  
Shuangsheng Wu ◽  
Jie Lin ◽  
Zhenyu Zhang ◽  
Yushu Yang
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Agglomerative Hierarchical Clustering ◽  
Research Gaps ◽  
Judicial Practice ◽  
Linguistic Term ◽  
Clustering Effect ◽  
Hierarchical Clustering Algorithm ◽  
Fuzzy Linguistic

The fuzzy clustering algorithm has become a research hotspot in many fields because of its better clustering effect and data expression ability. However, little research focuses on the clustering of hesitant fuzzy linguistic term sets (HFLTSs). To fill in the research gaps, we extend the data type of clustering to hesitant fuzzy linguistic information. A kind of hesitant fuzzy linguistic agglomerative hierarchical clustering algorithm is proposed. Furthermore, we propose a hesitant fuzzy linguistic Boole matrix clustering algorithm and compare the two clustering algorithms. The proposed clustering algorithms are applied in the field of judicial execution, which provides decision support for the executive judge to determine the focus of the investigation and the control. A clustering example verifies the clustering algorithm’s effectiveness in the context of hesitant fuzzy linguistic decision information.

scholarly journals Over/Undervoltage and Undervoltage Shift of Hybrid Islanding Detection Method of Distributed Generation

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Manop Yingram ◽  
Suttichai Premrudeepreechacharn
Keyword(s):  
Control System ◽  
Distributed Generation ◽  
Hybrid Method ◽  
Detection Method ◽  
System Simulation ◽  
Detection Methods ◽  
Islanding Detection ◽  
Active Method ◽  
Passive Method ◽  
Simulation Results

The mainly used local islanding detection methods may be classified as active and passive methods. Passive methods do not perturb the system but they have larger nondetection zones, whereas active methods have smaller nondetection zones but they perturb the system. In this paper, a new hybrid method is proposed to solve this problem. An over/undervoltage (passive method) has been used to initiate an undervoltage shift (active method), which changes the undervoltage shift of inverter, when the passive method cannot have a clear discrimination between islanding and other events in the system. Simulation results on MATLAB/SIMULINK show that over/undervoltage and undervoltage shifts of hybrid islanding detection method are very effective because they can determine anti-islanding condition very fast.ΔP/P>38.41% could determine anti-islanding condition within 0.04 s;ΔP/P<-24.39% could determine anti-islanding condition within 0.04 s;-24.39%≤ΔP/P≤ 38.41% could determine anti-islanding condition within 0.08 s. This method perturbed the system, only in the case of-24.39% ≤ΔP/P ≤38.41% at which the control system of inverter injected a signal of undervoltage shift as necessary to check if the occurrence condition was an islanding condition or not.

A bio-inspired hierarchical clustering algorithm with backtracking strategy

2014 ◽  
Vol 42 (2) ◽  
pp. 174-194 ◽  
Author(s):  
Akil Elkamel ◽  
Mariem Gzara ◽  
Hanêne Ben-Abdallah
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Hierarchical Clustering Algorithm
Sign in / Sign up

Export Citation Format

Share Document