Data-mining tools help you sift through vast quantities of information looking for valuable patterns in the data. A pattern may be as simple...
Data-mining tools help you sift through vast quantities of information looking for valuable patterns in the data. A pattern may be as simple as the realization that 80% of male diapher buyers also buy beer. Data mining is the process of discovering unsuspected patterns. Some early data-mining pioneers are reporting 1000% return on investment.
Data mining looks for patterns and groupings that you may have overlooked in your own analysis of a set of data. The tool typically performs a “fuzzy” search, with little or no guidance from an end user. In data mining, the tool does the discovery and tells you something, instead of you asking it a question. In contrast, query tools and OLAP return records that satisfy a query that you must first formulate. Instead of responding to low-level queries, data mining tools deal with fuzzy searches. They don’t assume that you know exactly what you’re seeking. Most tools use the following search methods:
If
Age = 42; and
Car_Make = Volvo; and
No_of_Children < 3
Then
Mailorder_Response = 15%
Data mining looks for patterns and groupings that you may have overlooked in your own analysis of a set of data. The tool typically performs a “fuzzy” search, with little or no guidance from an end user. In data mining, the tool does the discovery and tells you something, instead of you asking it a question. In contrast, query tools and OLAP return records that satisfy a query that you must first formulate. Instead of responding to low-level queries, data mining tools deal with fuzzy searches. They don’t assume that you know exactly what you’re seeking. Most tools use the following search methods:
- Associations look for patterns where the presence of something implies the presence of something else. For example: “Scuba gear buyers are good candidates for Australian vacations.
- Sequential patterns look for chronological occurrences. For example: the price of stock X goes up by 10%, the price of stock Y goes down by 15% a week later”.
- Clustering’s look for groupings and high-level classifications. For example: “Over 70% of undecided voters have incomes of over Rs.60000, age brackets between 40 and 50, and live in XYZ neighborhood.”
If
Age = 42; and
Car_Make = Volvo; and
No_of_Children < 3
Then
Mailorder_Response = 15%