Abstract-Problem of decision making, especially in financial issues is a crucial task in every business. Profit Pattern mining hit the target but this job is found very difficult when it is depends on the imprecise and vague environment, which is frequent in recent years. The concept of vague association rule is novel way to address this difficulty. Merely few researches have been carried out in association rule mining using vague set theory. The general approaches to association rule mining focus on inducting rule by using correlation among data and finding frequent occurring patterns. In the past years data mining technology follows traditional approach that offers only statistical analysis and discovers rules. The main technique uses support and confidence measures for generating rules. But since the data have become more complex today, it's a requisite to find solution that deals with such problems. There are certain constructive approaches that have already reform the ARM. In this paper, we apply concept of vague set theory and related properties for profit patterns and its application to the commercial management to deal with Business decision making problem.
Stock market collects huge amount of data which is uncertain, insufficient or fuzzy in nature.To make predictions for such data is very complicated task and one of the biggest challenges to the AI community. Various traditional and statistical indicators have been proposed for this. However, combination of these tools and techniques requires highly human expertise and so much justification in the area. Stock market behavior is highly suspecible. To increase performance of prediction there is a need of method which can accurately predict stock price and can train multiple records simultaneously. Neural Network is very important tool for stock market prediction. This paper mainly highlights the Neural Network based approach to predict stock market behavior and also helps the stock brokers and investors to invest money in stock market business at the right time.
In these days, stock market forecasting is one of the most interesting issues, which has gained a more attention due to vast profits. To precisely predict the price of share and making profits has been always challenging task since the longest period of time. This has engrossed the interest and attention of stock brokers, economists and applied researchers. Traditional methods like Fundamental analysis, Technical analysis, and Regression methods are not suitable for this task because these tools and techniques are based on totally different analytical approaches and requiring highly expertise and justification in the area. In this sequence, Association Rule Mining is one of the most interesting research areas for finding the associations, correlations among items in a database. It can discover all useful patterns from stock market dataset. The aim of this research study is to help stock brokers, investors so that they can earn maximum profits for each trading.
Researchers are used of data mining to extract hidden information from raw data. Now data mining can be used in any domain such as education. Data mining is used in education to achieve quality education and to categorize the students' performance through the analysis of educational data which reside or store in educational organization's database. In this paper, we categorize the performance of students based on their previous records such as 12 th marks, graduation marks, previous semester marks (PSM) , previous academic records (PAR-average of 12 th and graduation marks), mid sem marks (MSM), attendance (ATT) and end semester marks (ESM). Based on these attributes we determine the performance of students in end semester using apriori algorithm. With the help of categorization of performance, the main advantage is that classify of weak students, so that teacher give the particular interest on weak students and they could better perform in the next semester exam.
Stock market nature is considered to be dynamic and susceptible to quick changes because it depends on various factors like share price, fundamental variables like P/E ratio, dividend yield etc. election results, rumors etc. Now a day's prediction is an important process which determines the future worth of a company. The successful prediction brings motivation and awareness in stock community as well as economic growth of the country. In past various theories and methods like Efficient Market Hypothesis (EMH), Random Walk Theory, fundamental and technical analyses have been proposed. These methods or combination of methods have not got as much success even yet because these methods are very complex and time consuming and performed well on short data. These days stock market users mostly rely on intelligent trading system which would be help them to predict share prices based on various situations and conditions. Data mining is a broad area and also supports various business intelligence techniques. It has mastery to raise various financial issues like buying/selling security, bond analysis, contract analyses etc. in this study various prediction techniques like linear regression, multiple regression, association rule mining, clustering, neural network have been proposed and their significant performances will be compared by Bombay Stock Exchange (BSE) data.
This paper symbolizes our aim to promote Business organizations to take marketing decisions based on mining large databases of Transactions. Frequent item sets and Strong Association Rules are formed without need of supplying minimum support and minimum confidence. We have proposed and implemented an algorithm that scans Database only once and modifies Apriori algorithm and produces better results. General TermsAlgorithms.
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