Informatics Publishing Limited

Sachin Kamley , Shailesh Jaloree , R. S. Thakur

Abstract

For the last few decades’ education data mining has become one among foremost promising research areas. The only objective of this area is to explore data mining methods in order to analyze the student performance as well as impart the quality education for enhancing the performance of educational institutes. Data mining is the core part of the knowledge discovery process which is used to extract meaningful information from raw data. However, the various data mining techniques are proposed for achieving the most effective quality results. An Association Rule Mining (ARM) one of the well-known and popular data mining techniques which has been used extensively for educational perspective. In this study, higher education institute i.e. Government Girls College (GGC) data are considered and various attributes regarding student performance are analyzed for study purpose. Therefore, the various experiments based on support and confidence measures like 2%, 4%, 10%, 20% and 40% are conducted to generate interesting rules.  The major objective of this research study is to find the weaker students as well as those students who have bright performance in schooling level but could not be performed well on current semester exams due to certain reasons. However, teachers as well as parents can give particular attention to those students, whether they will perform better in the next semester or exams.

Keywords

Apriori Algorithm, Association Rule Mining, Confidence, Education Data Mining, Preprocessing, Prediction, Support, Weka 3.7.

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Sanjeev Gour ¹, Shailesh Jaloree ², Mamta Gour ³

Affiliations
1 Department of Computer Science, L. B. S. College of Professional Studies, Harda - 461331, Madhya Pradesh, IN
2 Department of Applied Mathematics, S. A. T. I. Engineering College, Vidisha - 464001, Madhya Pradesh, IN
3 Department of Chemistry, Government P. G. College, Harda - 461331, Madhya Pradesh, IN

Abstract

Objective: The objective of the this study is to analyze the various water quality parameters of the Narmada River and to find hidden relationship between them so that it suggest some decision plans or policies to predict or classify the water quality. Methods: In this study we find an approach to water quality management through Association or correlation studies between various water quality parameters. The Data Mining Technique called Association Rule Mining (Apriori Algorithm) is used to find and extract some rules or relationship between various water quality parameters for Narmada River at Harda and Hoshangabad districts of Madhy Pradesh. Findings: We have found some interesting and useful correlation between different water quality parameters and also we measure the performance of algorithm by the confidence and lift value factors. Application: This research present a model with actual data both for spatial and temporal patters and benefits of employing data mining techniques towards the improvement of water quality management plans. These results conclude that there is urgent need of strict regulatory monitoring for water quality maintenance in the river system at Hoshangabad District.

Keywords

Apriori Algorithm, Association Rule Mining

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Sachin Kamley ¹, Shailesh Jaloree ², R. S. Thakur ³

Affiliations
1 Department of Computer Applications, S.A.T.I., Vidisha - 464001, Madhya Pradesh, IN
2 Department of Applied Math’s and Computer Science, S.A.T.I., Vidisha - 464001, Madhya Pradesh, IN
3 Department of Computer Applications, M.A.N.I.T., Bhopal - 462003, Madhya Pradesh, IN

Abstract

Background/Objectives: Now a day’s stock market has great influence in the daily life of people. The biggest problem of stock market is that it holds large uncertainties which are directly related to their long term and short term investment. The key objective of researchers in this area is to strengthen economy as well as profit maximization on individual share. Methods/Statistical Analysis: This study, adopted a most popular back propagation training algorithm for predicting the performance of Bombay Stock Exchange of India based on long term and short term basis. In this study, 3500 samples of nine different companies are considered are divided 60% for training, 20% for validation and remaining 20% for testing task. In the initial phase the data underwent five phases which includes variable selection, data preprocessing, training dataset, prediction and evaluation. However the research evaluated through MATLAB tool which consists of training performance, error rate, and output prices. After getting the output from various evaluations made on stock dataset. Findings: It is shown by experimental results the method has given more accurate prediction results. Besides, it takes less training time and epochs for prediction task. It is also found that long term investment also has more successive returns over short term investment. Applications/Improvements: In the present study back propagation method is employed for long term and short term prediction. The different experimental results are carried out on stock data sets. It is also observed by study for long span of time small scale and medium scale companies like HCL and Ambuja Cement have better performance compared to large scale companies like TCS. In contrast, short span of time large scale companies like TCS also have better performance.

Keywords

BSE, Long Term Investment, Multi Layer Perceptron, Prediction, Short Term Investment

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