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Chitraa, V.
- Clustering of Navigation Patterns using Bolzwano_Weierstrass Theorem
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Authors
Affiliations
1 CMS College of Science & Commerce (Autonomous), Coimbatore, Tamilnadu, IN
2 Computer Science, NGM College (Autonomous), Pollachi, Coimbatore, Tamilnadu, IN
1 CMS College of Science & Commerce (Autonomous), Coimbatore, Tamilnadu, IN
2 Computer Science, NGM College (Autonomous), Pollachi, Coimbatore, Tamilnadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 12 (2015), Pagination:Abstract
Objectives: The primary objective of this research paper is to design a new and efficient clustering technique to group user navigation patterns which are useful for classification system to classify a new user with the previous users group. Methodology: Three real time web log data sets are collected from e-commerce web server, academic institution web server and a research journal web server. All three sets were collected from IIS web servers. After navigation patterns are derived from preprocessing step it is clustered into groups by using traditional Fuzzy C-Means technique. The clusters are validated and re-clustered using Bolzano_Weierstrass Theorem. Findings: Web log data is preprocessed and ICA is applied in the user session matrix to select relevant and important features. To measure the clustering accuracy of proposed and the existing methods, the parameters such as Rand Index, F measure are calculated and compared. It shows proposed BWFCM have higher rand index rate than FCM and lesser error rate. To understand the impact of the feature selection method, the data sets were implemented with the existing and proposed methods of feature selection. The parameters taken for comparison were Rand Index, Sum of Squared Errors, F-measure. The method was implemented in all the three data sets after data cleaning, session construction step. Clustering was carried out twice with the proposed clustering algorithm in all the three data sets, without selecting features and after selecting features. It was observed that the clustering results are poor when applied in full data set with irrelevant features, and the performance was increased after relevant features were selected. Conclusion: The result of the optimized clustering proves its significance and there is an increase in similarity of intra clustering and dissimilarity in inter clustering than the existing methods.Keywords
Bolzano_Weierstrass Theorem, Clustering, Feature Selection, Navigation Patterns, Web Usage MiningFull Text
- Digital Image Processing Technology for Measuring Yarn Hairiness in the Field of Textile
Abstract Views :203 |
PDF Views:5
Authors
R. Sudha
1,
V. Chitraa
1
Affiliations
1 Department of Computer Science, CMS College of Science & Commerce, Coimbatore, Tamil Nadu, IN
1 Department of Computer Science, CMS College of Science & Commerce, Coimbatore, Tamil Nadu, IN
Source
Digital Image Processing, Vol 10, No 1 (2018), Pagination: 7-11Abstract
This paper aims to review the recent trends in measuring yarn hairiness and the digital description, impartial assessment of yarn appearance are processed under the new approaches named digital image processing technology. The traditional detection methods and this new developed method were associated with each other which are made and analysed. When compared with the traditional methods, image-based methods have the advantages of being objective, fast and precise. Consequently, it perceived the yarn under a microscope and procurement a trace of hairs proved that the new trends are created in yarn appearance evaluation. The various indirect techniques for measuring yarn hairiness are developed, regarding the growth in its high commercial use of yarn increases. An endeavour was made in this work too reduce the risk and improve to automate the task using image processing technology. The first step is to develop the relevant algorithm proficient of analysing yarn hairiness image .The second step to minimize the need of requirement in this process of image actuation. In this work the best yarn hairiness indicator is recommended than traditional methods definition.Keywords
Hairiness Measurement, Hairiness Modeling, Yarn Spinning, Image Processing, Segmentation, Yarn Hairiness.
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- Study on Recent Research and Developments on Hairiness of Cotton Yarn by Image Processing Technique
Abstract Views :169 |
PDF Views:3
Authors
R. Sudha
1,
V. Chitraa
1
Affiliations
1 Department of Computer Science CMS College of Science and Commerce, Coimbatore, Tamilnadu, IN
1 Department of Computer Science CMS College of Science and Commerce, Coimbatore, Tamilnadu, IN
Source
Digital Image Processing, Vol 10, No 2 (2018), Pagination: 21-26Abstract
Fibers protruding out from the main body of the yarn are called Yarn Hairiness. It is one of the main aspects, key indicator of limitation to detect the yarn quality. It affects the appearance of yarn and subsequent processing of textile process. It is in most circumstances an undesirable property, giving rise to problem of fabric production and also deteriorates the fabric appearance. Various developments regarding yarn hairiness have been described in the various researches. These researchers handle the various aspects such as hairiness measurement, modelling, simulation, spinning modifications and post spinning treatments to reduce hairiness and many techniques for detecting also. This study is an attempt to analytically review all significant recent growth and progresses regarding yarn hairiness, further possibilities of research and future work are also concisely discussed.