P A College of Engineering

About: P A College of Engineering is a based out in . It is known for research contribution in the topics: Dihedral angle & Ring (chemistry). The organization has 298 authors who have published 594 publications receiving 4888 citations.

Data Categorization Using Hadoop MapReduce-Based Parallel K-Means Clustering

Abstract: The volume of datasets is increasing in a very fast rate due to the expansion of digitalization of each file of work. The traditional clustering algorithm becomes ineffective in analyzing such huge volume of datasets as it requires large time to cluster such huge volume of datasets. The parallel and distributed architectures are designed to process such large datasets. In order to obtain efficiency in clustering job, traditional clustering algorithms are required to be designed for such parallel and distributed architectures. Few parallel clustering algorithms are designed for gaining efficiency in clustering which works on datasets which are loaded and accessed from main memory, which in turn develops a limitation in clustering large datasets that cannot load millions of data objects in memory at once. In this work, we have proposed a parallel version of traditional K-means so as to execute it over Hadoop distributed framework. The experimental results show that our proposed K-means algorithm outperforms traditional K-means while clustering large volume of datasets.

A fuzzy neural network based framework to discover user access patterns from web log data

Abstract: Clustering data from web user sessions is extensively applied to extract customer usage behavior to serve customized content to individual users. Due to the human involvement, web usage data usually contain noisy, incomplete and vague information. Neural networks have the capability to extract embedded knowledge in the form of user session clusters from the huge web usage data. Moreover, they provide tolerance against imperfect and noisy data. Fuzzy sets are another popular tool utilized for handling uncertainty and vagueness hidden in the data. In this paper a fuzzy neural clustering network (FNCN) based framework is proposed that makes use of the fuzzy membership concept of fuzzy c-means (FCM) clustering and the learning rate of a modified self-organizing map (MSOM) neural network model and tries to minimize the weighted sum of the squared error. FNCN is applied to cluster the users’ web access data extracted from the web logs of an educational institution’s proxy web server. The performance of FNCN is compared with FCM and MSOM based clustering methods using various validity indexes. Our results show that FNCN produces better quality of clusters than FCM and MSOM.

3-(3,4-Dimethoxyphenyl)-1-(4-fluorophenyl)prop-2-en-1-one

Abstract: The title compound, C17H15FO3, crystallizes in a centrosymmetric space group and thus does not show any non-linear optical activity. There are two molecules in the asymmetric unit.