Association Rule Mining Technique With Improve Fp _ Tree And Frequent Closed Patterns

“Databases, Data Mining and Beyond” is the title of the article by Shamsul Chowdhurry. Professor Chowdhurry is a professor of Information Systems at Roosevelt University. The article focuses on case based reasoning, data mining and databases and their uses and application in various industries. First, let us define case based reasoning. Case based reasoning is a procedure to solve current problems by adapting solutions from past problems and applying it to the current problem to be solved. Case base database can be designed on case base reasoning. The structure of the database would have four steps. The steps include: 1) retrieval of similar cases; 2) adaptation; 3) evaluation and 4) update the case base. Each of the steps is a process that assists in the solving

Data mining is a very important component in today’s big data [22, 23]. Data mining is essential for everyone from large businesses to government organizations. It helps to identify trends, patterns and make predictions by exploring, comparing, researching and analyzing data.

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

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.

The current paper has used the association rule mining task on student database to predict the students division which is based on the previous performance. In this paper the simple Apriori algorithm has been used in WEKA for association rule generation, this may helpful to predict the future performance of the student and based on the necessary care can be taken for the students having poor

Rule induction is one of the major forms of data mining and is perhaps the most common form of knowledge discovery in unsupervised learning systems. It is also perhaps the form of data mining that most closely resembles the process that most people think about when they think about data mining, namely “mining” for gold through a vast database. The gold in this case would be a rule that is interesting - that tells you something about your database that you didn’t already know and probably weren’t able to explicitly articulate (aside from saying “show me things that are interesting”). The bane of rule induction systems is also its strength - that it retrieves all possible interesting patterns in the database. This is strength in the sense that it leaves no stone unturned but it can also be viewed as a weakness because the user can easily become overwhelmed with such a large number of rules that it is difficult to look through all of them. You almost need a second pass of data mining to go through the list of interesting rules that have been generated by the rule induction system in the first place in order to find the most valuable gold nugget amongst them all. This overabundance of patterns can also be problematic for the simple task of prediction because all possible patterns are culled from the database there may be conflicting predictions made by equally interesting rules. Automating the process of culling the most interesting rules and of combing the recommendations

In the year of 2001 when the use of data mining in marketing was a relatively new concept Shaw ,Subramaniam, Tan and Welge gave an insight about management of large database using data mining techniques. They brought the concept of identifying useful information from the large customer database by identifying hidden patterns. They integrated data mining and marketing knowledge management to help in managing marketing decisions.

More than one algorithms & sequences were predicted for top-k association administers mining. But, most of them do now not take advantage of the essential definition of an association rule. As an occurrence, KORD discovers approaches with an unmarried thing in the resulting, while the arrangement of principles of You et al. mines connection rules from a move in lieu of an exchange database. To the wonderful of our concentration, least difficult best k rules finds top-k affiliation rules predicated on a similar old meaning of an alliance run (with various things, in an exchange database). the primary pivotal thought process that characterize this calculation is that it characterizes the endeavor of mining the

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).

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.

Abstract— Data mining is a logical process that is used to search through large amount of data in order to find useful data [2].There are many different types of analysis that can be done in order to retrieve information from big data. Each type of analysis will have a different impact or result. Which type of data mining technique you should use really depends on the type of business problem that you are trying to solve.

Data mining is often referred to as “analytical intelligence” and is helping organizations for a better view of their business, to understand their customer needs and increase the effectiveness of the organization in the long run.

Abstract— Data mining is logical process that is used to extract or “mining” large amount of data in order to find useful data [2]. Knowledge discovery from Data or KDD is synonym for Data Mining[13].There are many different types of techniques that can be used to retrieve information from large amount of data. Each type of technique will generate different results. The type of data mining technique that should be selected depends on the type of business problem that we are trying to solve.

The term DM was conceptualised as early as 1990s as a means of addressing the problem of analysing the vast repositories of data that are available to mankind, and being added to continuously. DM has been the oldest yet one of the interesting buzzwords. It involves defining associations, or patterns, or frequent item sets, through the analysis of a given data set. Further-more, the discovered knowledge should be valid, novel, useful, and understandable to the user. Many organizations often underutilize their already existing databases not knowing that there is slot of hidden information that requires to be discovered i.e. interesting patterns or knowledge from these databases. DM disciplines revolve around statistics, artificial intelligence, and pattern recognition. There are two main techniques in DM that is reporting and DM techniques. Our study focuses on semi-automatic DM technique for discovering meaningful relationships from a given data set. There is no hypothesis required to mine the data (Jans 09). The technique uses exploratory analysis with no predetermined notions about what will constitute an ―interesting outcome (Kantardzi 02).

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 useless 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, correlation and association through complex algorithms and analysis, it has, over the past few decades proved to be a useful tool in cyber or internet security.