Data mining Assignment Help

Introduction

It refers to the mining and discovery of new information in terms of rules and patterns from vast amount of data. Practically it is carried out to be on large database and files. Data mining is integrated with RDBMS except to DBMS. Extracting the hidden predictive information from large database which is a new technology with the great potential that help companies to focus on most important information in there data warehouse. Data mining tools predict future trends and allowing business to make knowledge-driven decisions. As most companies already collect large databases and refine that massive quantities of data. This technique implemented on the company’s existing software and hardware platform to enhance the value of existing information resources so that they can be integrated with the new products.

Goals of Data Mining

The goals fall into following classes:

• Prediction: prediction shows how certain attributes within the data will behave in future. Ex: certain seismic wave pattern can predict earthquake with the high probability in scientific context.
• Identification: Identification done in data pattern by identify the existing item, an event or an activity .Eg: violator can try be break a system may be identified by the files accessed, and CPU time per session. To identify certain sequence of nucleotides symbols in the DNA sequence they are known authentication.
• Classification: Data Mining can partition the data into categories so that we can identified later based on parameters. Eg: Take an example of supermarket in which customer can be categorized into many different categories like a customer with the discount seeking shopper, shopper in a rush, loyal regular shopper, infrequent shopper and so on and the classification based on domain knowledge is used as input to the mining problem.
• Optimization: Eventually one of the single goal of data mining can be optimized if we limit our resources like time, space, money, or material and maximize the output like sales or profit and that all done under the set of constraints.

Advantages of Data Mining

• Marketing or Retail: Data mining helps to marketing companies in a way to get the response of direct mail, online marketing campaign to build historical data as model based. Because of this result now marketers have better approach to sell there products to only targeted customers.
• Finance or Banking: It gives financial institutional information about loans and credit information by building a model of historical data with the customer records so that they can determine good and bad loans. Also, Data mining helps customer to detect fraudulent credit card transaction to protect authenticate owners.
• Manufacturing: Manufactures can detect faulty equipment in operational engineering data by applying data mining. It has been applying to determine the range of control parameters then those optimal parameters used to manufacture desired quality equipment.
• Governments: It helps government agencies by digging and analysing records to build patterns that can detect criminal activities.

Disadvantages of Data Mining

• Privacy Issues: As internet is booming with the with social networks, e-commerce, forums and blogs people are afraid of their personal information is collected and used in unethical way that cause trouble. Business collect information about their customers in different ways to understand their purchasing behaviour trends.
• Security Issues:Security is a big issue now these days. Businesses own information of there employee and customer but how properly this information carried out is still in question.
• Inaccurate Information: Information is collected through data mining and that intended for the ethical purpose can be misused and can be exploited by unethical businesses to take benefits of people.

Important data mining software

Today data mining are used in many areas to make system intelligent, to get the important analysis. However manual analysis is hard for tonnes of data. Lots os software are used for data analysis and data mining if you are looking for help in following data mining software then please contact us.

• GMDH Shell help
  
• Data Applied help
• GeneXproTools help
• Senn help
• OmniAnalyser help
• ANGOSS KnowledgeSTUDIO help
• Weka help
• KNIME
• RapidMiner
• Orange
• SAS
• Data Mining And Knowledge Discovery