Data Mining Research Paper

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Data Mining

Determine the benefits of data mining to the businesses when employing:

Predictive analytics to understand the behaviour of customers

"The decision science which not only helps in getting rid of the guesswork out of the decision-making process but also helps in finding out the perfect solutions in the shortest possible time by making use of the scientific guidelines is known as predictive analysis" (Kaith, 2011). There are basically seven steps involved in the predictive analysis, these are: spotting the business problem, exploring various data sources, extracting patterns from data, building a sample model by making use of the data and problem, Clarify data -- finding valuable factors -- generating new variables, constructing a predictive model by making use of sampling and validating and deploying the model.

Decisions can be made very quickly by the business if they make use of this method as, they will have a lot of data to help them in their decision making process. Predictive analysis has three main benefits which are: pursuing new sources of revenues, minimizing risk and identifying fraud. Some of the examples of these benefits are being able to predict the risks associated with the credit and loan organizations as well as being able to make the predictions about coupons and promotional offers. Algorithm of this kind helps the business by testing all sorts of scenarios and situations that it will actually take them years to test in the practical world. It helps in reducing the costs associated with making mistakes. Businesses are also able to achieve competitive edge by being able to study and observe the consumer behaviour.

b. Associations discovery in products sold to customers

Association analysis can be of great use when relationships need to be found out in huge amounts of data. Two things that have to be kept in mind when making use of the association analysis with respect to the market data are:

1. It can prove to be computationally very expensive to discover patterns from the large amounts of transaction data.

2. There is a possibility that most of the discovered patterns can prove to be false as they may have happened only be chance.

A very common example of association discovery is the marker-base analysis. This algorithm is made use of in the recommendation engines. These are the engines that observe the products that the customers have shown interest in or that they have already bought and based on that they recommend other products to the customers.

c. Web mining to discover business intelligence from Web customers

The process of getting structured information from semi-structured or unstructured web data sources is known as web data mining. Web data mining is used by the companies in order to collect data from various websites and assemble it in a way that analysis can be done or websites can be built which will gather and provide information from various other websites. In order to survive in this competitive market it has become very important for the businesses to make use of web mining which would help them in getting a better understanding of the market and competitors by getting information from various websites, which will ultimately help these businesses in catering to the needs of these customers in a better way.

It is very important to make use of the web mining in order to manage the web sites in an effective manner along with creating business and support services, adaptive web sites, analysis of the network traffic flow, personalization etc. Business intelligence helps the businesses in staying up-to-date with the trends of the markets, customer demands as well as giving information about new ways to generate more revenue.

d. Clustering to find related customer information

A market is subdivided into separate subsets of customers with the help of cluttering analysis and from these subsets any one of them can be chosen as a target market which can be reached with the help of specific marketing mix. This analysis basically works in such a manner that it looks for clusters of data that are like one another and then put that similar data into a segment (, 2008).

Today information about what sites the customers go to and in what order do they view the pages is recorded…[continue]

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