The field of data mining, like statistics, concerns itself with “learning from data” or “turning data into information”. In this article we will look at the. Chapter 1. STATISTICAL METHODS FOR DATA MINING. Yoav Benjamini. Department of Statistics, School of Mathematical Sciences, Sackler Faculty for Exact. Data Mining techniques often use statistical exploratory data analysis tools.
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Let us briefly illustrate this sequence.
Data Mining and Statistics: What is the Connection?
Data are what we can capture and store e. Information relates items of data e.
X lives data mining statistics Z; S is Y years old; X and S moved; W has money in Zand becomes knowledge when it is used in the successful completion of the decision process.
Hence knowledge relates items of information e. As we see, the main problem is to know how to get from data to knowledge, or, as J.
Keep in mind, however, that the data mining techniques and tools are equally applicable in fields ranging from law enforcement to radio astronomy, medicine and industrial process control to name some of the fields.
It is widely recognized that data mining statistics of all sizes need to learn to emulate what small, service-oriented businesses have always done well — creating one-to-one relationships with their customers.
In every industry, forward-looking companies are trying to move towards the one-to-one ideal of understanding each customer individually and to use that understanding data mining statistics make it easier for the customer to do business with them rather than with a competitor.
These same companies are learning to look at the lifetime value of each customer so they know which ones are worth investing money and effort to hold on to and which ones to let data mining statistics. As noted, a small business builds one-to-one relationships with its customers by noticing their needs, remembering their preferences, and learning from past interactions how to serve them better in the future.
Data Mining and Statistics: What is the Connection? |
How can a large enterprise accomplish something similar when most customers may never interact personally with company employees? What can replace the creative intuition of the sole proprietor who recognizes customers by name, face, and voice, and remembers their habits and preferences?
But through the clever application of information technology, even the largest enterprise data mining statistics come surprisingly close. In large commercial enterprises, the first step — noticing what data mining statistics customer does — has already largely been automated.
On-line transaction processing OLTP systems are everywhere, collecting data on seemingly everything. These days, we all go through life generating a constant data mining statistics of transaction records. The customer-focused enterprise regards every record of an interaction with a client or prospect as a learning opportunity.
But, learning requires more than simply gathering data.
Difference between Data Mining and Statistics
In fact, many companies gather hundreds of gigabytes or terabytes of data from and about their customers without learning anything.
Data is gathered because it is needed for some operational purpose, e. And, once it has served that purpose, it data mining statistics on tape or gets discarded. For learning to take place, data from many sources must first be gathered together and organized in a consistent and useful way.
Statistics and Data Mining
This is called data warehousing. Hence data warehousing allows the enterprise to remember what it has noticed about its customers. Data warehousing provides the enterprise with a memory.
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