Importance Of Association Rule Mining

CHAPTER 1
INTRODUCTION
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This chapter gives the overview of the Association Rule Mining. It gives the importance of the Market Basket Analysis and its usefulness in increasing the sales of the supermarket. This chapter also provides an overview of the data mining process used in market basket analysis and the proposed approaches. The works of a few scientists are cited and utilized as proof to confidence the ideas clarified in the theory. Every such proof utilized is recorded as a part of the reference area of this thesis.
1.1 OVERVIEW
Currently the world has a wealth of data, stored all over the planet (the Internet and Web are prime examples), but it is needed to be understand that data. It has been

Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others

It comprises of data clearing, for instance, handling missing values and elimination of noise or outliers. It will possibly involve using complex statistical techniques or a data mining algorithm.
Data Integration
The integration is one of the most significant features of data warehouse. Here, multiple data sources may be integrated. Data is given from multiple dissimilar sources into the data warehouse. As the data is fed, it is transformed, reformatted, summarized, and so forth. The result of integration is that once the data exists in the data warehouse, it has a particular physical corporate image. In all the integration architecture, there are several difficulties that come up when attempting to integrate data from different sources.
Data Selection
Once the data elements are chosen from several sources, it is essential to examine the value of the data. Data samples are accumulated from the sources and data profiling is carried out to recognize the issues of physical data quality. The data which are selected for an object are dependent on the patterns of significance. The data acquired from the sources will be required for three major purposes during the data mining process i.e. training the data mining model, testing it and applying it on the entire