The Theory Of Association Rule Mining

Data mining is defined as the process of data selection and studying and building models using massive data stores to disclose previously unidentified patterns in databases (Koh and Tan, 2005, p. 64). Koh and Tan have found financial institutions, marketers, manufactures and so has many other agencies have used data mining. Data mining has been of great use by various organizations. For example, data mining has been useful to detect fraudulent credit card transactions (Koh and Tan, 2005, p. 64). Koh and Tan stated, “In healthcare, data mining is becoming increasingly popular, if not increasingly essential” (Koh and Tan, 2005, p.64). In healthcare there have been reports that data mining has been successful in detecting fraud and abuse in healthcare claim (Koh and Tan, 2005, p.65). There are many factors in healthcare that have driven the use of data mining applications. One of the factors that have driven healthcare to use the data mining applications will be the medical insurance fraud and abuse. All organizations currently involved in the healthcare industry can profit from the data mining applications. For example, data mining is able to help

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

What is data mining? Data mining is the deriving new information from massive amounts of data in databases (Sauter, 2014, p. 148). Chowdhurry argues that data mining is part of KDD. KDD is knowledge discovery in databases, it is a process that includes data mining. In addition to data mining, KDD includes data preparation, modeling and evaluation of KDD. KDD is at the heart of this research field. This research field is multidisciplinary and includes data visualization, machine learning, database technology, expert systems and statistics. Overall, the use of a case based reasoning and data mining tools within an information system would create a CBR system to solve new problems with adapted solutions and could be used in many industries such as education and healthcare (Chowdhurry,

Before a data set can be mined, it first has to be ?cleaned?. This cleaning process removes errors, ensures consistency and takes missing values into account. Next, computer algorithms are used to ?mine? the clean data looking for unusual patterns. Finally, the patterns are interpreted to produce new knowledge.3

Across a wide variety of fields, data emanating from the massive healthcare insurance providers such as government and private companies in healthcare are being collected and stored at tremendous pace. Thus, there is a need felt by most of the companies to manage their wealth of knowledge. Hence, due to the tremendous increase in data, extracting useful information from that data became important. Thus, to extract useful information from the database, Knowledge Discovery in the Database (KDD) is needed. Therefore, KDD is defined as a process of identifying valuable, important, useful and understandable patterns from a large complex database (Maimom & Rokach, 2007).

There are various techniques involve in the recommender systems. The popular one that is concerned to solve the existing problems is data mining techniques. Many recommender systems implement data mining technique as a method to analyze and discover significant consumer knowledge. Mohammed et al. (2013) and Kumar Guptaa and Guptab (2010) state that data mining is defined as a systematical process of exploring and discovering valuable knowledge from large volume of data in data repositories. Data mining aims to extract and disclose hidden vital patterns from data. It has potential to collect relevant data in large volume of database and data warehouse. Additionally, the data mining can use to anticipate future customer trends and help business to gain competitiveness (Liao, Chen, and Lin, 2011).

This paper represents the Information Systems Decision-Making course and will address the following two issues.

This master thesis addresses the data mining area known as closed itemset mining. The work program includes analysis a one of well-known algorithms from the literature, and then modifying these algorithm in order to optimize their performance by reduce the number of frequent pattern.

Data mining works or performs these feats using a technique that called modeling. Modeling is simply the act of building model in one application where there is an answer and then we apply it to another situation that you don’t. This act of model building has been doing by people for a long time, certainly it before the advent

With rapid advancements in the technology, new concepts are hitting the industry and it is redefining itself over a course of time. The data mining is one of its kind to improvise the lives of people. Data mining uses techniques which are helpful in finding out the different forms of data. The data mining is closely related to the database technology. Almost every industry takes the help of the datamining to grow in their respective fields. For instance, stock management, quality control, risk management, fraud detection, marketing and analysis of investments. It has its applications ranging from finding the molecule structure of the gene to identifying a robbery at an international level.

This research paper highlight the importance and need of data mining in the age of electronic media where large amount of information and consolidated database is readily available. This seemingly useful information can unearth some mind-blowing statistics and predict the future trends with relative ease through use of data mining techniques which can benefit the businesses, start-ups, country and individual alike. However, since data mining is effective in bringing out patterns, alerts, correlation and association through complex algorithms and analysis, it has, over the past few decades proved to be a useful

Background - One of the most promising developments in the field of computing and computer memory over the past few decades has been the ability to bring tremendous complex and large data sets into database management that are both affordable and workable for many organizations. Improvement in computer power has also allowed for the field of artificial intelligence to evolve which also improves the sifting of massive amounts of information for appropriate use in business, military, governmental, and academic venues. Essentially, data mining is taking as much information as possible for a variety of databases, sifting it intelligently and coming up with usable information that will help with data prediction, customer service, what if scenarios, and extrapolating trends for population groups (Ye, 2003; Therling, 2009).

Data mining is a relatively new phenomenon, therefore the number of peer-reviewed journal articles, blogs and other online sources on the topic are limited but growing rapidly. One key book, Data Mining and Analysis: Fundamental Concepts and Algorithms by Zaki and Meira Jr., takes an algorithmic approach, as the title suggests. Zaki and Meira Jr. define data mining by stating that “data mining comprises the core algorithms that enable one to gain fundamental insights and knowledge

Association Rule- Association is the most popular data mining techniques and fined most frequent item set. Association strives to discover patterns in data which are based upon relationships between items in the same transaction. Because of its nature, association is sometimes referred to as “relation technique”. This method of data mining is utilized within the market based analysis in order to identify a set, or sets of products that consumers often purchase at the same time

These necessities have prompted the conception of Data Mining that has been changing the live from the data age toward the coming information age. A considerable amount of literature has been published on Data Mining and the aim of this survey is concerned with the ideas behind the processes; purpose and techniques of Data Mining. [1][2]