A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Sreedevi, M.
- Modeling of Tweet Summarization Systems using Data Mining Techniques: A Review Report
Authors
1 Department of Computer Science and Engineering, KL University,Vaddeswaram 522502, Andhra Preadesh, IN
Source
Indian Journal of Science and Technology, Vol 9, No 44 (2016), Pagination:Abstract
Objective: Data mining is the driving force for analysing and summarization of available data in various forms and restores it for further needs. Summarization of various literature studies have been done by the researchers based on tweets and its summarization method applied on the datasets has to be identified for analysis. Methods/Statistical Analysis: The analysis has been in learning the methods or techniques used from the literature of various researches in gathering knowledge of various tweets datasets used and the way in which they have analysed the datasets from small tweets of unstructured to the large blogs. Findings: Various pro and cons of techniques and methods used by the researchers are identified as to the knowledge for better development of new methods for fast and accurate data analysis on tweets and blog. Application/Improvements: The paper gives us an idea to data experts and user how to prevent issues of tweets and various methods used for tweets analysis timely for summarization and data analysis.Keywords
Clustering, Micro-Blogging, Summarization, Timeline Generation, Tweets.- An Improved Prediction of Kidney Disease using SMOTE
Authors
1 K L University, Guntur - 522502, Andhra Pradesh, IN
2 Department of Computer Science and Engineering, K L University, Guntur - 522502, Andhra Pradesh, IN
Source
Indian Journal of Science and Technology, Vol 9, No 31 (2016), Pagination:Abstract
Objectives: This article presents a framework to improve the accuracy of rule induction and decision tree models.Analysis: In this paper, we used a rebalancing algorithm called SMOTE to enhance the accuracy of different induction and decision tree models in order to predict kidney disease of patients. For this prediction, data collected from Apollo Hospitals, Tamil Nadu, India has been analysed. Findings:In this research, initial dataset is not balanced i.e. most of the instances belong to the same class. If dataset is imbalanced, the traditional models can’t produce accurate results. Thus the proposed framework improves the accuracy of models by balancing the imbalanced dataset. For this, a technique for sampling the minority class called SMOTE is applied on existing dataset and percentage of variation between classes is minimized. The examined findings with various classifiers algorithms and with the use of over sampling algorithm,the produced findings proves an increasing accuracy and also those results are compared with balanced and imbalanced dataset. In particular, this method can attain the average accuracy of 98.73%. Applications:This method can be applied in other areas to improve the accuracy in case of imbalanced dataset. In case of Big Data also SMOTE can be applied using Hadoop framework and Mapreduce programming model with new algorithmic approach.
Keywords
Classification, Data Mining, Health Informatics, Kidney Failure, SMOTE.- Mining Closed Regular Patterns in Data Streams
Authors
1 Department of Computer Science and Engineering, K. L. University, Guntur, Andhra Pradesh, IN
2 Department of Computer Science and Engineering, LBR College of Engineering, Mylavaram, Andhra Pradesh, IN
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 5, No 1 (2013), Pagination: 171-179Abstract
Mining regular patterns in data streams is an emerging research area and also a challenging problem in present days because in Data streams new data comes continuously with varying rates. Closed item set mining gained lot of implication in data mining research from conventional mining methods. So in this paper we propose a narrative approach called CRPDS (Closed Regular Patterns in Data Streams) with vertical data format using sliding window model. To our knowledge no method has been proposed to mine closed regular patterns in data streams. As the stream flows our CRPDS-method mines closed regular itemsets based on regularity threshold and user given support count. The experimental results show that the proposed method is efficient and scalable in terms of memory and time.Keywords
Data Streams, Regular Patterns, Closed Regular Patterns, Transaction Sliding Window.- Data Warehousing Practices in Business Initiatives
Authors
1 School of Computing, K.L.College of Engineering, Vaddeswaram, Guntur (D.T) Andhrapradesh, IN