Informatics Publishing Limited

V. Laxmi Narasamma ¹, M. Sreedevi ¹

Affiliations
1 Department of Computer Science and Engineering, KL University,Vaddeswaram 522502, Andhra Preadesh, IN

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.

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Sai Prasad Potharaju ¹, M. Sreedevi ²

Affiliations
1 K L University, Guntur - 522502, Andhra Pradesh, IN
2 Department of Computer Science and Engineering, K L University, Guntur - 522502, Andhra Pradesh, IN

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.

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M. Sreedevi ¹, L. S. S. Reddy ²

Affiliations
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

Abstract

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.

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G. Vijay Kumar ¹, M. Sreedevi ¹

Affiliations
1 School of Computing, K.L.College of Engineering, Vaddeswaram, Guntur (D.T) Andhrapradesh, IN

Abstract

The paper presents the data warehousing architecture and practices used at a major. Retailing company. Many considerations were assessed when deciding which data warehousing architecture to adopt. The paper discusses the two pre-dominant styles in data warehousing, namely the “Bill Inmon Style” or the top-down approach and the “Ralph Kimball Style” or the bottom-up approach. The com-pany chose the Inman style due to a unique combination of circumstances in their business and technical environments, which are being discussed in detail. Much of the information presented in this paper is based upon the direct experiences of the lead data architect assigned to the projects under which this retailing company’s customer data warehouse evolved. The architecture has evolved over time and currently has been accepted at the company as a best practice. It is interesting to mention that both the hardware platform (CPU and disk drives) and Relational Database Management System (RDBMS) soft-ware employed today at this company for data ware-housing is not the same as was selected for the first instantiation. The implication was that the best plan or practice was a flexible one. There were many challenges, like organizational, technical, data sourcing and data naming, needed to be solved during the pre-project, initial stages, and throughout the project and beyond. The initial data warehouse, implemented in 1996, was termed an overall success and approved for expansion. The current data warehouse data are being used by over six hundred registered users to fine-tune customer marketing and leverage and share data in an enterprise manner. The data warehouse has allowed the company to strengthen customer relation-ship management (CRM) core capabilities and business partnerships. Today, there are many departments benefiting from queries and requests for data warehouse data, many anticipated, some not.

Keywords

Data Warehouse, Business Intelligence, CRM.

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