2. Background and Literature Review
The purpose of this chapter is to show an in depth review of the topics, areas and works related to the research presented here. we conduct a brief but comprehensive in depth review of data mining and association rule mining approaches and techniques, followed by a focus at interestingness & quality, and redundancy issues related to association rule mining. This review sets the basic work for our research and the proposals made here.
2.1 Data mining
Data Mining technique is the result of a long process of studies and research in the area of databases and product development. This evolution began when business data and companies was stored for the first time on computer device, with continuous
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2.2 Frequent Pattern Mining
In this part, we introduce frequent pattern mining by giving an overview of the problem, including a formal definition, the description of some practical applications and a survey of the most renowned and influential algorithms proposed for solving this problem.
2.2.1 Overview
Frequent pattern mining and association rule mining were first introduced in 1993 by Agrawal et al. [15]. Informally speaking, association rules can be seen as if-then rules: e.g. if a person buys cheese, he or she also buys beer. A measure that is often associated with association rule mining is that of support: the some of customers for whom the rule holds, or rather the relative number of customers buying all items occurring in the rule (the so-called underlying pattern or itemset) [16]. Basically, the objective is to find those items in a data set that commonly co-occur, based on a certain minimum support value. Besides itemsets, it 's also possible to mine more complex patterns, such as trees and graphs.
2.2.2 Definition The formal definition of frequent pattern mining and association rule mining in a relational setting is the following:
Let db be a transaction table with schema R = {I1,I2……. In}, in which each Ii is a binary attribute. The attributes in db correspond to items and the rows in db correspond to
Data mining is another concept closely associated with large databases such as clinical data repositories and data warehouses. However data mining like several other IT concepts means different things to different people. Health care application vendors may use the term data mining when referring to the user interface of the data warehouse or data repository. They may refer to the ability to drill down into data as data mining for example. However more precisely used data mining refers to a sophisticated analysis tool that automatically dis covers patterns among data in a data store. Data mining is an advanced form of decision support. Unlike passive query tools the data mining analysis tool does not require the user to pose individual specific questions to the database. Instead this tool is programmed to look for and extract patterns, trends and rules. True data mining is currently used in the business community for market ing and predictive analysis (Stair & Reynolds, 2012). This analytical data mining is however not currently widespread in the health care community.
Data mining software allows users to analyze large databases to solve business decision problems. Data mining is, in some ways, an extension of statistics, with a few
Today with the ever growing use of computers in the world, information is constantly moving from one place to another. What is this information, who is it about, and who is using it will be discussed in the following paper. The collecting, interpreting, and determination of use of this information has come to be known as data mining. This term known as data mining has been around only for a short time but the actual collection of data has been happening for centuries. The following paragraph will give a brief description of this history of data collection.
Abstract - In the Data mining process, we can identify the patterns in the data that is hard to find using normal analysis. Several Mathematical and statistical algorithms are used in this approach to determine the probability of the event or scenario. The main aim of this process in terms of technical representation is to find the correlation amongst the attributes. There is a huge amount of discovery being carried out in this field creating a huge scope and jobs in this area. Several data mining algorithms are present that could determine different features present in the data that could lead in prediction and future analysis. Main Study report would consist of these algorithms that could help us predict and some sample data that we
In its infancy, data mining was as limited as the hardware being used. Large amounts of data were difficult to analyze because the hardware simply could not handle it [1]. The term "data mining" first began appearing in the 1980 's largely within the research and computer science communities. In the 1990 's it was considered a subset of a process called Knowledge Discovery in Databases of KKD [1]. KKD analyzes data in the search for patterns that may not normally be recognized with the naked eye. Today however, data mining does not limit itself to databases,
Data mining allows companies to focus on the more important information in their data warehouses. Data mining can be broken down into two major categories. Automated prediction of trends and behaviors, and automated discovery of previously unknown patterns. In the first category, data mining automates the process of finding predictive information in large databases. Questions that traditionally required exhaustive hands-on analysis can now be quickly answered directly from data. In the second category, data mining tools sweep through databases and identify previously hidden patterns in one step. This category is where the major focus of research has been on.
Lying hidden in all this data is information— potentially useful information—that is rarely made explicit or taken advantage of. Data mining activity for pattern deployment is achieved automatically or semi automatically (Ian, Frank & Hall 2011). The realised patterns lead to some economic advantage. Patterns can also be expressed or as a transparent box constructed to reveal the structure of the pattern with the assumption that the realised patterns make good predictions.
Business analysts engage in Business Intelligence (BI) initiatives to derive useful information out of of raw data. One popular BI technique is data mining. This research paper overviews Business Intelligence and its history, followed by an in-depth discussion on data mining, including its functional framework, its popular models, end-users, issues and trends. The paper also
However, these techniques lead recommender systems face with the important problems such as sparsity, precision, and scalability problem. Thus, applying data mining techniques to the recommender systems is concerned as a solution for solve this problem (Deuk et al., 2011). Its capability could play a significant role for analyzing and predicting valuable customer knowledge, for instance, purchase behaviors, customer preferences, and interests. Also, then using that knowledge for suggesting products/services that suit and satisfy customers (Kumar Guptaa and Guptab, 2010).
Frequent itemsets play an main role in a lot of data mining tasks that try to get interesting patterns in databases, such as association rules, clusters, sequences correlations, episodes and classier. Although the number of all frequent itemsets is usually very large, the subset that is really interesting for the user typically contains only a small number of itemsets. Therefore, the model of constraint-based mining was introduced. Constraints provide focus on the interesting knowledge, thus decrease the number of patterns extracted to those of possibility interest. Additionally, they can be
Data mining is the process of releasing concealed information from a large set of database and it can help researchers gain both narrative and deep insights of exceptional understanding of large biomedical datasets. Data mining can exhibit new biomedical and healthcare knowledge for clinical decision making. Medical assessment is very important but complicated problem that should be performed efficiently and accurately. The goal of this paper is to discuss the research contributions of data mining to solve the complex problem of Medical diagnosis prediction. This paper also reviews the various techniques along with their pros and cons. Among various data mining techniques, evaluation of classification is widely adopted for supporting medical diagnostic decisions.
Data mining applications are designed not to rely on real time data, but on archived historical data because they allow mathematically sophisticated technique for analyzing data from
Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data. There is a huge amount of data available in the Information Industry. This data is of no use until it is converted into useful information. It is necessary to analyse this huge amount of data and extract useful information from it. Extraction of information is not the only process we need to perform; data mining also involves other processes such as Data Cleaning, Data Integration, Data Transformation, Data Mining, Pattern Evaluation and Data Presentation [12].
Association rule mining can be used to extract patterns of a website visitors’ behavior. This data can be used to improve web marketing (e business)techniques or to improve the web surfing experience. Here we are applying association rule on web usage log file of an institution. We are using association rule as a interesting measures and verifying their values in two different period of time. We will see how this comparison brings extra important information about association rules generation and helps a webmaster make more and more accurate decisions about the website development and enhancements.