Top Research area's in data mining is given below.
- Developing a unifying theory of data mining
- Scaling up for high dimensional data and high speed data streams
- Mining sequence data and time series data
- Mining complex knowledge from complex data
- Data mining in a network setting
- Distributed data mining and mining multi-agent data
- Data mining for biological and environmental problems
- Data Mining process-related problems
- Security, privacy and data integrity
- Dealing with non-static, unbalanced and cost-sensitive data
Overview
Generally, data mining (sometimes called data or knowledge
discovery) is the process of analyzing data from different perspectives
and summarizing it into useful information - information that can be
used to increase revenue, cuts costs, or both. Data mining software is
one of a number of analytical tools for analyzing data. It allows users
to analyze data from many different dimensions or angles, categorize it,
and summarize the relationships identified. Technically, data mining
is the process of finding correlations or patterns among dozens of
fields in large relational databases.
Continuous Innovation
Although data mining is a relatively new term, the technology is
not. Companies have used powerful computers to sift through volumes of
supermarket scanner data and analyze market research reports for years.
However, continuous innovations in computer processing power, disk
storage, and statistical software are dramatically increasing the
accuracy of analysis while driving down the cost.
Example
For example, one Midwest grocery chain used the data mining capacity of Oracle
software to analyze local buying patterns. They discovered that when
men bought diapers on Thursdays and Saturdays, they also tended to buy
beer. Further analysis showed that these shoppers typically did their
weekly grocery shopping on Saturdays. On Thursdays, however, they only
bought a few items. The retailer concluded that they purchased the beer
to have it available for the upcoming weekend. The grocery chain could
use this newly discovered information in various ways to increase
revenue. For example, they could move the beer display closer to the
diaper display. And, they could make sure beer and diapers were sold at
full price on Thursdays.
Data, Information, and Knowledge
Data
Data are any facts, numbers, or text that can be processed by a
computer. Today, organizations are accumulating vast and growing amounts
of data in different formats and different databases. This includes:
- operational or transactional data such as, sales, cost, inventory, payroll, and accounting
- nonoperational data, such as industry sales, forecast data, and macro economic data
- meta data - data about the data itself, such as logical database design or data dictionary definitions
Information
The patterns, associations, or relationships among all this data can provide information. For example, analysis of retail point of sale transaction data can yield information on which products are selling and when.
Knowledge
Information can be converted into knowledge about historical
patterns and future trends. For example, summary information on retail
supermarket sales can be analyzed in light of promotional efforts to
provide knowledge of consumer buying behavior. Thus, a manufacturer or
retailer could determine which items are most susceptible to promotional
efforts.
Data Warehouses
Dramatic advances in data capture, processing power, data
transmission, and storage capabilities are enabling organizations to
integrate their various databases into data warehouses. Data
warehousing is defined as a process of centralized data management and
retrieval. Data warehousing, like data mining, is a relatively new term
although the concept itself has been around for years. Data warehousing
represents an ideal vision of maintaining a central repository of all
organizational data. Centralization of data is needed to maximize user
access and analysis. Dramatic technological advances are making this
vision a reality for many companies. And, equally dramatic advances in
data analysis software are allowing users to access this data freely.
The data analysis software is what supports data mining.
What can data mining do?
Data mining is primarily used today by companies with a strong
consumer focus - retail, financial, communication, and marketing
organizations. It enables these companies to determine relationships
among "internal" factors such as price, product positioning, or staff
skills, and "external" factors such as economic indicators, competition,
and customer demographics. And, it enables them to determine the impact
on sales, customer satisfaction, and corporate profits. Finally, it
enables them to "drill down" into summary information to view detail
transactional data.
With data mining, a retailer could use point-of-sale records of
customer purchases to send targeted promotions based on an individual's
purchase history. By mining demographic data from comment or warranty
cards, the retailer could develop products and promotions to appeal to
specific customer segments.
For example, Blockbuster Entertainment mines its video rental
history database to recommend rentals to individual customers. American
Express can suggest products to its cardholders based on analysis of
their monthly expenditures.
WalMart is pioneering massive data mining to transform its
supplier relationships. WalMart captures point-of-sale transactions from
over 2,900 stores in 6 countries and continuously transmits this data
to its massive 7.5 terabyte Teradata
data warehouse. WalMart allows more than 3,500 suppliers, to access
data on their products and perform data analyses. These suppliers use
this data to identify customer buying patterns at the store display
level. They use this information to manage local store inventory and
identify new merchandising opportunities. In 1995, WalMart computers
processed over 1 million complex data queries.
The National Basketball Association (NBA) is exploring a data
mining application that can be used in conjunction with image recordings
of basketball games. The Advanced Scout
software analyzes the movements of players to help coaches orchestrate
plays and strategies.
For example, an analysis of the play-by-play sheet of the game played
between the New York Knicks and the Cleveland Cavaliers on January 6,
1995 reveals that when Mark Price played the Guard position, John
Williams attempted four jump shots and made each one! Advanced Scout
not only finds this pattern, but explains that it is interesting because
it differs considerably from the average shooting percentage of 49.30%
for the Cavaliers during that game.
By using the NBA universal clock, a coach can automatically bring
up the video clips showing each of the jump shots attempted by
Williams with Price on the floor, without needing to comb through hours
of video footage. Those clips show a very successful pick-and-roll play
in which Price draws the Knick's defense and then finds Williams for an
open jump shot.
