SP. Malarvizhi

Work place: Sri Vasavi Engineering College, Tadepalligudem, Andhra Pradesh, India

E-mail: spmalarvizhi1973@srivasaviengg.ac.in


Research Interests: Computer systems and computational processes, Computational Learning Theory, Data Mining, Data Structures and Algorithms


Dr.SP.Malarvizhi received the BE degree in Electrical and Electronics Engineering from Annamalai University, India in 1994, ME degree in Computer Science and Engineering from Anna University of Technology, Coimbatore, India in 2009 and received the Ph.D. degree in Data Mining from Anna University Chennai in 2016. She is currently working as an Associate Professor in CSE department at Sri Vasavi Engineering College, Andhra Pradesh since 2017. She has participated and published papers in many National and Internal Conferences and also published 7 papers in National and International journals. Her research interests are Data Mining, Big Data and Machine Learning.

Author Articles
House Price Prediction Modeling Using Machine Learning

By M. Thamarai SP. Malarvizhi

DOI: https://doi.org/10.5815/ijieeb.2020.02.03, Pub. Date: 8 Apr. 2020

Machine Learning is seeing its growth more rapidly in this decade. Many applications and algorithms evolve in Machine Learning day to day. One such application found in journals is house price prediction. House prices are increasing every year which has necessitated the modeling of house price prediction. These models constructed, help the customers to purchase a house suitable for their need. Proposed work makes use of the attributes or features of the houses such as number of bedrooms available in the house, age of the house, travelling facility from the location, school facility available nearby the houses and Shopping malls available nearby the house location. House availability based on desired features of the house and house price prediction are modeled in the proposed work and the model is constructed for a small town in West Godavari district of Andhrapradesh. The work involves decision tree classification, decision tree regression and multiple linear regression and is implemented using Scikit-Learn Machine Learning Tool.

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Recent and Frequent Informative Pages from Web Logs by Weighted Association Rule Mining

By SP. Malarvizhi

DOI: https://doi.org/10.5815/ijmecs.2019.10.05, Pub. Date: 8 Oct. 2019

Web Usage Mining provides efficient ways of mining the web logs for knowing the user’s behavioral patterns. Existing literature have discussed about mining frequent pages of web logs by different means. Instead of mining all the frequently visited pages, if the criterion for mining frequent pages is based on a weighted setting then the compilation time and storage space would reduce. Hence in the proposed work, mining is performed by assigning weights to web pages based on two criteria. One is the time dwelled by a visitor on a particular page and the other is based on recent access of those pages. The proposed Weighted Window Tree (WWT) method performs Weighted Association Rule mining (WARM) for discovering the recently accessed frequent pages from web logs where the user has dwelled for more time and hence proves that these pages are more informative. WARM’s significance is in page weight assignment for targeting essential pages which has an advantage of mining lesser quality rules.

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