An Improved Fuzzy Clustering Method for Text Mining

Deng, Jiankang; Hu, Juanli; Chi, Hehua; Wu, Juebo

doi:10.1109/nswctc.2010.23

Cited by 24 publications

(9 citation statements)

References 7 publications

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“…From a machine learning point of view, clustering corresponds to the unsupervised discovery of hidden patterns presented in the dataset to represent a data structure. Clustering plays a prominent role in the analysis of data in such areas as text mining [DHCW10], web analysis [YD11], marketing [TSD09], medical diagnostics [Alb03], and many others. There are many different clustering algorithms which can be categorized based on the notation of a cluster.…”

Section: Clusteringmentioning

confidence: 99%

Detection of zero-day malware based on the analysis of opcode sequences

Zolotukhin

Hämäläinen

2014

2014 IEEE 11th Consumer Communications and Networking Conference (CCNC)

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Esitetään Jyväskylän yliopiston informaatioteknologian tiedekunnan suostumuksella julkisesti tarkastettavaksi yliopiston vanhassa juhlasalissa S212 toukokuun 22. päivänä 2014 kello 12.Academic dissertation to be publicly discussed, by permission of the Faculty of Information Technology of the University of Jyväskylä, in building Seminarium, auditorium S212 on May 22, 2014 at 12 o'clock noon. UNIVERSITY OF JYVÄSKYLÄ JYVÄSKYLÄ 2014On This work focuses on the application of different methods and algorithms of data mining to various problems encountered in mobile networks and computer systems. Data mining is the process of analysis of a dataset in order to extract knowledge patterns and construct a model for further use based on these patterns. This process involves three main phases: data preprocessing, data analysis and validation of the obtained model. All these phases are discussed in this study. The most important steps of each phase are presented and several methods of their implementation are described. In addition, several case studies devoted to different problems in the field of computer science are presented in the dissertation. Each of these studies employs one or more data mining techniques to solve a posed problem. Firstly, optimal positions of relay stations in WiMAX multihop networks are calculated with the help of genetic algorithm. Next, the prediction of the next mobile user location is carried out based on the analysis of spatial-temporal trajectories and application of several classifying methods. After that, the use of clustering and anomaly detection techniques for the detection of anomalous HTTP requests is presented. Finally, the data mining approach is applied for the detection and classification of malicious software. These case studies show that data mining methods can help to solve many different problems related to mobile networking and network security. LIST OF FIGURES LIST OF TABLES

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Section: Clusteringmentioning

confidence: 99%

Detection of zero-day malware based on the analysis of opcode sequences

Zolotukhin

Hämäläinen

2014

2014 IEEE 11th Consumer Communications and Networking Conference (CCNC)

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“…Memberships and typicality's are important for the correct feature of data substructure in clustering problem. Thus, an objective function in the FPCM depending on both memberships and typicality's can be represented as below: (9) with the following constraints:…”

Section: Fuzzy Possibilistic C-means Algorithm For Clustering (Fpcm)mentioning

confidence: 99%

“…Jiabin Deng et al, [9] proposed an improved fuzzy clusteringtext clustering method based on the fuzzy C-Means clustering algorithm and the edit distance algorithm. The author used the feature evaluation to reduce the dimensionality of highdimensional text vector.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Fuzzy Possibilistic C-Means Algorithm using EM Algorithm (EMFPCM)

Shanthi¹,

Suganya²

2013

IJCA

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The major difficulties that arise in several fields, comprising pattern recognition, machine learning and statistics, is clustering. The basic data clustering problem might be defined as finding out groups in data or grouping related objects together. A cluster is a group of objects which are similar to each other within a cluster and are dissimilar to the objects of other clusters. The similarity is typically calculated on the basis of distance between two objects or clusters. Two or more objects present inside a cluster and only if those objects are close to each other based on the distance between them. In order to provide better clustering approaches that fits for all applications and to improve the efficiency of data clustering, this paper proposes a effective clustering techniques called Enhancement of Fuzzy Possibilistic C-Means Algorithm using EM Algorithm (EMFPCM). Thus with the help of EMFPCM, noise is reduced, provides more accuracy and thus provides better result in predicting the user behavior. The algorithm was implemented and the experiment result proves that this method is very effective in predicting user behavior. This approach is suitable for applications in business, such as to design personalized web service. The performance of the proposed approaches is evaluated on the UCI machine repository datasets such as Iris, Wine, Lung Cancer and Lymphograma. The parameters used for the evaluation is Clustering accuracy, Mean Squared Error (MSE), Execution Time and Convergence behavior. Bradley et al.,[8] put forth a technique for the purpose of refining initial points for clustering algorithms, in particular K- RELATED WORK

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“…It is important to detect whether the cause is an excessive income of food associated with a limited physical activity or an effect of metabolic problems, such as low fat-burning rate or an increased level of insulin in the blood. Therefore, it is convenient to group patients suffering from obesity and then, on the basis of medical examinations, describe a typology of people depending on the type of metabolism [3,13], eventually recommending an individual treatment [11]. Such examinations could be based on metabolic energometry tests, bioimpedance measurements [9], blood analyzes, and other methods that are assessed subjectively without standardization [16,17].…”

Section: Introductionmentioning

confidence: 99%

Automatic Fuzzy Classification System for Metabolic Types Detection

Prauzek

Hlavica

Michalíková

2015

Advances in Intelligent Systems and Computing

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Patients suffering from obesity have different demands for medical treatment regarding the causes of their metabolic disorders. To propose new medical solutions to weight reduction, it is desirable to group patients exhibiting similar characteristics. This contribution describes an automatic fuzzy classification system capable of dividing obese patients into groups of diverse metabolic types. Metabolic data were acquired through energometry tests and bioimpedance measurements. Methods considered in this paper are particularly Principal Component Analysis used for data set's reduction and fuzzy clustering method dividing patients into groups called clusters. Newly tested patients are then classified into designed clusters. A set of statistical hypothesis testing methods is eventually applied to verify the performed classification. The designed classification system could be applied in hospitals to help the doctors with design of an individual treatment for obese patients' groups.

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An Improved Fuzzy Clustering Method for Text Mining

Cited by 24 publications

References 7 publications

Detection of zero-day malware based on the analysis of opcode sequences

Detection of zero-day malware based on the analysis of opcode sequences

Enhancement of Fuzzy Possibilistic C-Means Algorithm using EM Algorithm (EMFPCM)

Automatic Fuzzy Classification System for Metabolic Types Detection

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