Data Mining - Efficient in Detecting and Solving the Fraud Cases

Data Mining - Efficient in Detecting and Solving the Fraud Cases

Data mining can be considered to be the crucial process of dragging out accurate and probably useful details from the data. This application uses analytical as well as visualization technology in order to explore and represent content in a specific format, which is easily engulfed by a layman. It is widely used in a variety of profiling exercises, such as detection of fraud, scientific discovery, surveys and marketing research. Data management has applications in various monetary sectors, health sectors, bio-informatics, social network data research, business intelligence etc. This module is mainly used by corporate personals in order to understand the behavior of customers. With its help, they can analyze the purchasing pattern of clients and can thus expand their market strategy. Various financial institutions and banking sectors use this module in order to detect the credit card fraud cases, by recognizing the process involved in false transactions. Data management is correlated to expertise and talent plays a vital role in running such kind of function. This is the reason, why it is usually referred as craft rather than science.

The main role of data mining is to provide analytical mindset into the conduct of a particular company, determining the historical data. For this, unknown external events and fretful activities are also considered. On the imperious level, it is more complicated mainly for regulatory bodies for forecasting various activities in advance and taking necessary measures in preventing illegal events in future. Overall, data management can be defined as the process of extracting motifs from data. It is mainly used to unwrap motifs in data, but more often, it is carried out on samples of the content. And if the samples are not of good representation then the data mining procedure will be ineffective. It is unable to discover designs, if they are present in the larger part of data. However, verification and validation of information can be carried out with the help of such kind of module.

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Using Charts For Effective Data Mining

Using Charts For Effective Data Mining

The modern world is one where data is gathered voraciously. Modern computers with all their advanced hardware and software are bringing all of this data to our fingertips. In fact one survey says that the amount of data gathered is doubled every year. That is quite some data to understand and analyze. And this means a lot of time, effort and money. That is where advancements in the field of Data Mining have proven to be so useful.

Data mining is basically a process of identifying underlying patters and relationships among sets of data that are not apparent at first glance. It is a method by which large and unorganized amounts of data are analyzed to find underlying connections which might give the analyzer useful insight into the data being analyzed.

It's uses are varied. In marketing it can be used to reach a product to a particular customer. For example, suppose a supermarket while mining through their records notices customers preferring to buy a particular brand of a particular product. The supermarket can then promote that product even further by giving discounts, promotional offers etc. related to that product. A medical researcher analyzing D.N.A strands can and will have to use data mining to find relationships existing among the strands. Apart from bio-informatics, data mining has found applications in several other fields like genetics, pure medicine, engineering, even education.

The Internet is also a domain where mining is used extensively. The world wide web is a minefield of information. This information needs to be sorted, grouped and analyzed. Data Mining is used extensively here. For example one of the most important aspects of the net is search. Everyday several million people search for information over the world wide web. If each search query is to be stored then extensively large amounts of data will be generated. Mining can then be used to analyze all of this data and help return better and more direct search results which lead to better usability of the Internet.

Data mining requires advanced techniques to implement. Statistical models, mathematical algorithms or the more modern machine learning methods may be used to sift through tons and tons of data in order to make sense of it all.

Foremost among these is the method of charting. Here data is plotted in the form of charts and graphs. Data visualization, as it is often referred to is a tried and tested technique of data mining. If visually depicted, data easily reveals relationships that would otherwise be hidden. Bar charts, pie charts, line charts, scatter plots, bubble charts etc. provide simple, easy techniques for data mining.

Thus a clear simple truth emerges. In today's world of heavy load data, mining it is necessary. And charts and graphs are one of the surest methods of doing this. And if current trends are anything to go by the importance of data mining cannot be undermined in any way in the near future.

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