The Importance Of Knowledge Discovery, A Paramount Process In Data Mining

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 uses computer-based technology to evaluate data in a database and identify different trends. Effective data mining helps researchers predict economic trends and pinpoint sales prospects. Data mining is stored in data warehouses, which are sophisticated customer databases that allow managers to combine data from several different organization functions.

Data mining is a class of database applications that looks for hidden patterns in a group of data that can be

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.

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

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.

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

An organisation(data owner) which lacks the expertise or computational resources required for data mining can outsource its data mining tasks to third party service provider(server).But there are various security issues associated with this kind of outsourcing because the server can misuse the data provide by the organisation directly or by extracting frequents patterns from it. However, both data and the association

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

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).

Association Rule Mining is a part of data mining, which are most important techniques. Data Mining is used to extract the required information from a certain total data. Association Rules are mainly used in several areas such as telecommunication networks, risk management, etc.

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.

The creation of our graphical and Command Line interface came about as a means of testing our algorithms. Since we were attempting to mine associations from large datasets, it made sense to test our mining algorithms on artificially created datasets so as to check whether or not our algorithms were successful. Therefore, we devised the graphical interface to allow us to create sufficiently large datasets based upon association rules. This allowed us to plant specific rules with set supports and confidence with in our test datasets. We were then able to run our test datasets through our association mining algorithms to verify whether or not the algorithms correctly identified the rules which we had planted with the correct

After a study of literature review the concluded that the Association rule with Apriori Algorithm is used in combination of Fuzzy c means clustering and it gives result better Accuracy in web page prediction. Also we use Association rule with Apriori algorithm and Markov model in web page prediction then it may also result a highest probability in web page and helped to predict the user access behavior and also decrease the access time of user. So our new proposed methodology has following methods:

With the fast development of e-commerce (electronic commerce) sector in last few years, the importance and applicability of intelligent data management techniques has become essential in the e-business sector. The data about client behavior can play important role in