Data mining is the process of using one or many computer learning techniques to analyze data contained within a database. The database will most likely be contained in a data warehouse, which is repository that stores a massive amount of data from databases and other data sources. Data mining is used to find patterns and discover new information about the data in the database, which can then be used to make predictions and/or improve current systems. The results of data mining is hugely beneficial to business for obtaining competitive advantages and to research groups.
The accessibility of the data within the database that will be used while data mining is very important. Designing the database to have data stored into individual cases, and
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One example is with credit card fraud. Credit card companies often track the purchases of its card holders. By using data mining, credit card companies can use the purchase record of an individual card holder to create a model of their buying habits. If a purchase is made that does not correlate with your purchase model, then the credit card company is alerted of the possible theft of a card.
Data Mining can be performed in many different ways. Some standard methods of data mining includes: association, classification, clustering, prediction, sequential patterns, and decision trees. Artificial neural networking is one method of data mining that has performed well in various domains.
An artificial neural network is designed to process information in a way similar to how information is processed by the biological human brain. The human brain is primarily made up of neurons. These neurons are connected to each other with synapses (this connection is called a synaptic connection.) Through these connections, neurons are able to pass signals to and from each other. In simple terms, these signals are able to process patterns and behaviors. This is a very basic explanation of brain function. Artificial neural networks are also made up of “neurons”, which are called nodes, and data is passed to and from nodes through their connections with each
Data mining is continuingly growing in use throughout many different industries for a variety of business purposes. Some of these industries are financial and banking, healthcare, retail, etc. (Groth, 2000). One of the main uses of data mining is marketing a company’s products and or services to customers (Groth, 2000).
Databases always used to fascinate me from my under graduation with great curiosity to know how large data is managed and queried. This led me to do Masters in computer science concentrating in the field of Data Management. In the course of my study, I understood the concepts of DBMS which provides a robust and efficient way of managing and mining data. Through the courses like Database Systems (ITCS 6160), Knowledge Discovery in Databases(ITCS 6162) and Knowledge Based Systems(ITCS 6155) I gained enough theoretical and practical knowledge about the importance of proper organization of data, good techniques to build an efficient database management system and how well the data can be managed.
Abstract: The number of video games involving human computer interaction have increased significantly. When a user interacts with a machine through an interface, a feedback is generated which is either displayed as output or stored for further processing. Decision making by machines can involve large number of computations and simulations so that the computer's move is beneficent to its own game. This paper provides definitions of major areas of Artificial Intelligence and Computational Intelligence in gaming. Chess is an example of such a high level application of artificial and computational intelligence. Chess engines evaluate current position of the chess board and decide which the next best move is. Various approaches towards computing
Data mining is very famous technique for churn prediction and it is used in many fields. Data mining refers to the process of analyzing data in order to determine patterns and their relationships. It is an advanced technique which goes deep into data and uses machine learning algorithms to automatically shift through each record and variable to uncover the patterns and information that may have been hidden. There is a lot of work done in data mining for churn prediction in different fields. It is used to solve the customer churn problem by identifying the customer behavior from large number of customer data.
The benefits of a data warehouse are that it provides decision making information, organized in a way that facilitates the types of access required for that purpose and supported by a wide range of software designed to work with it.
There are many ways that data mining can be applied to your corporate data, which will provide greater insight into your business or operations. The value that data mining provides is knowledge about patterns or events that you may not know. As data storage technology advances and information systems continue to collect and process data, a treasure is amassing that is waiting to be discovered. Are you ready to make your claim and find your riches?
According to Queens University of Belfast, customer data mining is the practice of searching through mass amounts of customer data to find patterns that can better increase revenue and cut company costs. Customer data mining allows businesses to better organize and access data from customer interactions to find information on customer usage patterns.
Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others
There are various kinds of definitions about what data mining is. The authors in [1] define data mining as “the process of extracting previously unknown information from (usually large quantities of) data, which can, in the right context, lead to knowledge”. Data mining is widely used in areas such as business analysis, bioinformatics analysis, medical analysis, etc. Data mining techniques bring us a lot of benefits. Business companies can use data mining tools to search potential customers and increase their profits; medical diagnosis can use data mining to predict potential disease. Although the term “data mining” itself is neutral and has no ethical implications, it is often related to the analysis of information associated with individuals. “The ethical dilemmas arise when data mining is executed over the data of an individual” [2]. For example, using a user’s data to do data mining and classifying the user into some group may result in a variety of ethical issues. In this paper, we deal with two kinds of ethical issues caused by data mining techniques: informational privacy issues in web-data mining and database security issues in data mining. We also look at these ethical issues in a societal level and a global level.
n data mining, trends and patterns are identified on a huge set of data to discover knowledge. In such analysis, varieties of algorithms exist for extracting knowledge such as clustering, classification and association rule mining. Thus, association rules mining one domain for delivering knowledge on complex data. Moreover, the basis of the discovered association rules is usually determined by the minimum support s % and minimum confidence c% to represent the transactional items in database D. Thus, it has the implication of the form AB, where A is the antecedent and B is the consequent. The problem with such display of rules is the disclosure of sensitive information to the external part when data is shared. Hence Privacy Preserving in Data Mining (PPDM) related to Association Rules emerges.
Data mining is also called knowledge discovery in database. In computer science, the process of discovering knowledge and relationship in large amount of data. This field combines from statistics and artificial intelligence with database management known as data set.
...fman R. A. - "Data Mining and Knowledge Discovery" - A Review of issues and Multi- strategy Approach". Reports of the Machine Learning and Inference Laboratory, MCI 97-2, George Mason University, Fairfax, V.A. 1997. http://www.mli.gmu.edu/~kaufman/97-1.ps
Description: Data Mining contains of several algorithms that fall into four different categories(Shobana et al. 2015)
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.