Published on February 17, 2014
What is Data Mining? Data mining is known as Knowledge discovery data from Data, or KDD. ● It refers to extracting knowledge from large amounts of data. This sentence written by Jiawei Han, Micheline Kmaber. ● Generally speaking, Data mining is the method of finding correlations or models among dozens of fields in large relational databases. ●
KDD Process as per following steps: ● ● ● ● ● ● ● Data Cleaning Data integration Data Selection Data transformation Data mining Pattern Evaluation Knowledge presentation
Data Mining is the core of KDD Process
Where has it come from?
Types of Data Analysis: 1. Descriptive data mining – (business intelligence and data mining) 2. Predictive data mining - (optimization and simulation)
Figure: Types of Data Analysis
Types of Data Mining: ● Association Rules ● Classification ● Clustering ● Sequential Patterns ● Sequence Similarity
The Importance of Data mining: ● Every particular person will be looking for a pattern of database. One of the data mining tool is SAS (Statistical analysis system) tool that is used for business intelligence, data management and predictive anaytics. ● In area of science and business deal with a vast amount of data, which needs to be turned into knowledge. ●
Every business organization must collect a large amount of information like employee's data, sales data, report of market analysis and customer's information so it is necessary to utilize data mining because it plays a key role. ●
Advantages of Data Mining: Data mining plays very effective role in the marketing field. In today's era, every marketer need to build business models based on historical data to predict who will react to the new marketing campaigns such as direct email marketing and online marketing campaign...etc. Through this effort, marketers will find ideas to sell profitable products to targeted customers. ● ● Data mining is also helps in retail companies as per marketing field.
Data mining is used In finance/banking, manufacturing and government agency. ● In short means, it's useful for market based analysis and help to take good decision. ●
Disadvantages of Data Mining: ● User privacy and security issues. ● Misuse of information/inaccurate information ● Great cost of implementation stage
How data mining is useful in IT world? ● This is just an example how data mining can be useful. In today's era, there are many company offers online marketing services in various countries and then they need to use good loading website. But they are facing problem in times of loading home page. There are many professional QA tried to solve this problem but they could not solve the bug. IT companies are suffering from insufficient time for loading the home page for some users so the company gets to know. ●
As far as management is concerned, They have to solve the problem because response time of the website may decrease reduce sales. With the help of mining tool, IT department has already looked into log files. ● It has been explored that they are not facing single type typical problem. Instead data mining program explores that there are diversified problem clusters. ●
The patterns for this problem is defined by a combination of country codes, operating system and the version of a browser that the customer use. ● Due to different problem, another pattern also shows worse performance in loading time of a website. ● Loading time of the website depends on the speed of internet and cookie setting. This is how data mining helps in identifying the problem and therefore solving the problem becomes more immediate. ●
The future of Data mining: ● The future of data mining depends on three terms: Short term – Data mining will be profitable in business areas. Online marketing and advertising will target potential customers with new precision. ● Medium term – For email marketing, data mining will become a common and easy tool. ● Long term – Data mining will be excellent in long-term prospects. Mostly, It will be useful in medical research data. ●
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