Data Minning in the Contemporary World

To begin with, Dell software an information technology enterprises describes Data Mining as “an analytic process designed to explore data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the

Data Mining. It is the process of discovering interesting knowledge that are gathered and significant structures from large amounts of data stored in data warehouse or other information storage.

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.

In today’s society businesses accumulate all the data they can gather into the data warehouse, from which they can do data mining. This means that when we go to the grocery store and use our “saver card”, to get the tiny percent off, that store automatically tracks what you purchased and then enters it into their data warehouse. This allows the businesses to later go through that information and look for particular trends, seeing what products are popular what time of year, and as well as try figure what would we most likely to purchase. The art of looking through the data warehouse is referred to as data mining.

How data mining can assist bankers in enhancing their businesses is illustrated in this example. Records include information such as age, sex, marital status, occupation, number of children, and etc. of the bank?s customers over the years are used in the mining process. First, an algorithm is used to identify characteristics that distinguish customers who took out a particular kind of loan from those who did not. Eventually, it develops ?rules? by which it can identify customers who are likely to be good candidates for such a loan. These rules are then used to identify such customers on the remainder of the database. Next, another algorithm is used to sort the database into cluster or groups of people with many similar attributes, with the hope that these might reveal interesting and unusual patterns. Finally, the patterns revealed by these clusters are then interpreted by the data miners, in collaboration with bank personnel.4

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

Data mining: is a process of discovering patterns in large data involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems with the aim of extracting information and transforming it into an understandable structure for further use.

In its infancy, data mining was as limited as the hardware being used. Large amounts of data were difficult to analyze because the hardware simply could not handle it [1]. The term "data mining" first began appearing in the 1980 's largely within the research and computer science communities. In the 1990 's it was considered a subset of a process called Knowledge Discovery in Databases of KKD [1]. KKD analyzes data in the search for patterns that may not normally be recognized with the naked eye. Today however, data mining does not limit itself to databases,

Data mining is when a financial analyst gathers consumer information and looks for patterns that a business can exploit. A simplified data mining example is when a restaurant manager knows the local yearly convention schedule based on experience. The manager can cross-reference that information with historical sales results to predict such things as forecasted profit or labor demand. With this information, the manager can estimate an advertising budget or hire temporary staff to handle anticipated work load. When medium to large-sized businesses use data mining, they uncovering these same information points; however, revenue gains can range from millions to billions of dollars. There are several techniques that firms frequently employ to find gold in information.

Data, Data everywhere. It is a precious thing that will last longer than the systems. In this challenging world, there is a high demand to work efficiently without risk of losing any tiny information which might be very important in future. Hence there is need to create large volumes of data which needs to be stored and explored for future analysis. I am always fascinated to know how this large amount of data is handled, stored in databases and manipulated to extract useful information. A raw data is like an unpolished diamond, its value is known only after it is polished. Similarly, the value of data is understood only after a proper meaning is brought out of it, this is known as Data Mining.

Both data mining and data analysis are a subset of Business Intelligence which also includes data management systems, data warehouses and Online analytic processing(OLAP). To manage the mountains of information, the data is put away in a warehouse of information accumulated from different sources, including corporate databases, compressed data from interior frameworks, and information from outer

Data mining is the process through which previously unknown patterns in data were discovered. Another definition would be “a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and subsequent knowledge from large databases.” This includes most types of automated data analysis. A third definition: Data mining is the process of finding mathematical patterns from (usually) large sets of data; these can be rules, affinities, correlations, trends, or prediction models.

Data mining or Knowledge Discovery in Databases (KDD) is discovering patterns from large data groups through methods of artificial intelligence, machine learning ,statistics, and database systems. The aim of data mining process is to extract information from a data group and switch it to an ideal format for future . The data mining process comprise of database and data management aspects, data preprocessing, inference, complexity of discovered structures, and updating.

Data mining is a detection process that allows users to comprehend the substance and relationships amid the data. Data architect/designer punctiliously defines entities and relationships from operational or data warehouse system. The conclusion of data mining can be used to intensify the efficacy of performance from the users. Data mining uses various techniques such as inductive logic programming, pattern recognition, image analysis, bioinformatics, spatial data analysis, decision support systems etc. for this sort of analysis.

Data mining has become a key technology for companies and researchers in many fields , The number and diversity of applications is growing over the years it is expected a significant increase in this growth and there are many commercial space worked on DM prematurely recently been applied DM in all areas in the banking, insurance, retail, telecommunications and pharmacy, health and government and all e-business types and many of domain (Figure 2 ) Data mining applications in 2008(http://www. kdnuggets. com).,The authors highlight the importance of developing a appropriate back testing environment that become the collection of Enough evidence to convince the end users that the system can be used in practice