Data Mining Area Known As Closed Itemset Mining Essay

The output of an association rule mining algorithm is a set of association rules respecting the user-specified minsup and minconf thresholds.

Computing frequent itemset 1: Given the database transaction id and all itemsets generate the database transaction id,itemsets format.Apply hash function to identifyy the frequent item sets ,support value and bucket count .

Examine the time required to generate Frequent itemsets with 90 percent support on pumsb [11] dataset. The comparison is shown in Table VII.

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 is an analytical process that primarily involves searching through vast amounts of data to spot useful, but initially undiscovered, patterns. The data mining process typically involves three major steps—exploration, model building and validation and finally, deployment.

Data Mining, a sub-branch of computer science, involving statistics, methods and calculations to find patterns in large amount of data sets, and database systems. Generally, data mining is the process to examine data from different aspects and summarizing it into meaningful information. Data mining techniques depict actions and future trends, allowing any individual to make better and knowledge-driven decisions.[1][2]

With the increased and widespread use of technologies, interest in data mining has increased rapidly. Companies are now utilized data mining techniques to exam their database looking for trends, relationships, and outcomes to enhance their overall operations and discover new patterns that may allow them to better serve their customers. Data mining provides numerous benefits to businesses, government, society as well as individual persons. However, like many technologies, there are negative things that caused by data mining such as invasion of privacy right. This paper tries to explore the advantages as well as the disadvantages of data mining. In addition, the ethical and global issues regarding the use of data mining

In a world where computers are becoming as essential to daily life as the cars we drive or the telephones we use to communicate, it is difficult to find a person who doesn’t have some particular use for computers. Computers have become the information stores of the world. If you take a moment to think about all the kinds of information a person can and does hold on their computer it is staggering. I myself have all the passwords to my email and bank accounts, the history of every web page I’ve visited in the last 3 weeks, my credit card numbers, the complete history of all my banking transactions for the last three years stored on my computer. Additionally, think about all the

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.

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).

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

A data stream is a real time, continuous, structured sequence of data items. Mining data stream is the process of extracting knowledge from continuous, rapid data records. Data arrives faster, so it is a very difficult task to mine that data. Stream mining algorithms typically need to be designed so that the algorithm works with one pass of the data. Data streams are a computational challenge to data mining problems because of the additional algorithmic constraints created by the large volume of data. In addition, the problem of temporal locality leads to a number of unique mining challenges in the data stream case. The data mining techniques namely clustering, classification and frequent pattern mining are applied to extract the knowledge

Association rule mining was invented to extract patterns from transactional databases. As stated, an association rule is an method applied in the form X →Y, where X and Y are sets of items. Association rule mining finds all such conditions which

Data mining is an area of data processing in which extraction of useful patterns from pre-existing databases and transformation of extracted information into understandable form is done. Data mining employs various methods like clustering, classification, regression etc.[1] One such method is Association Rule Mining which discovers the dependencies between database variables. Frequent Pattern Mining is an area of data mining which works on this principle and generates

Mining closed itemset, initially proposed in [23] Pasquier et al .A-close algorithm is a basic algorithm in frequent closed itemsets mining which is based on Apriori algorithm[25] in frequent item mining. A-close operation is performed in the following two general steps: Producing frequent generators and achieving closure of frequent generators. For A-close algorithm, an itemset p is generator of closed itemset y, if p is one of smallest itemsets (it may be more than one), and it determines y with Galois closure operator h(p)=y [26]. To produce generators, a level-wise approach, similar to that of Apriori algorithm is taken. Then three steps of pruning are conducted on candidate generators, and useless generators are pruned thereby[26]. The operation of generator production is repeated until no other generator is produced. After producing generators G1 to Gn (n is maximum generator size), closure of all these frequent generators should be computed. The closure of all frequent generators results in all closed frequent itemsets. The technique for calculating closure as the next.