Data Mining Information
1 Define data mining. Why are there many different names and definitions for data mining?
Data mining is the process through which previously unknown patterns in data were discovered. Another definition would be “a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and subsequent knowledge from large databases.” This includes most types of automated data analysis. A third definition: Data mining is the process of finding mathematical patterns from (usually) large sets of data; these can be rules, affinities, correlations, trends, or prediction models.
Data mining has many definitions because it’s been stretched beyond those limits by some software vendors to include most forms of data analysis in order to increase sales using the popularity of data mining.
What recent factors have increased the popularity of data mining?
Following are some of most pronounced reasons:
More intense competition at the global scale driven by customers’ ever-changing needs and wants in an increasingly saturated marketplace.
General recognition of the untapped value hidden in large data sources.
Consolidation and integration of database records, which enables a single view of customers, vendors, transactions, etc.
Consolidation of databases and other data repositories into a single location in the form of a data warehouse.
The exponential increase in data processing and storage technologies.
Significant reduction in the cost of hardware and software for data storage and processing.
Movement toward the de-massification (conversion of information resources into nonphysical form) of business practices.
Is data mining a new discipline? Explain.
Although the term data mining is relatively new, the ideas behind it are not. Many of the techniques used in data mining have their roots in traditional statistical analysis and artificial intelligence work done since the early part of the
Data mining is the process through which previously unknown patterns in data were discovered. Another definition would be “a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and subsequent knowledge from large databases.” This includes most types of automated data analysis. A third definition: Data mining is the process of finding mathematical patterns from (usually) large sets of data; these can be rules, affinities, correlations, trends, or prediction models.
Data mining has many definitions because it’s been stretched beyond those limits by some software vendors to include most forms of data analysis in order to increase sales using the popularity of data mining.
What recent factors have increased the popularity of data mining?
Following are some of most pronounced reasons:
More intense competition at the global scale driven by customers’ ever-changing needs and wants in an increasingly saturated marketplace.
General recognition of the untapped value hidden in large data sources.
Consolidation and integration of database records, which enables a single view of customers, vendors, transactions, etc.
Consolidation of databases and other data repositories into a single location in the form of a data warehouse.
The exponential increase in data processing and storage technologies.
Significant reduction in the cost of hardware and software for data storage and processing.
Movement toward the de-massification (conversion of information resources into nonphysical form) of business practices.
Is data mining a new discipline? Explain.
Although the term data mining is relatively new, the ideas behind it are not. Many of the techniques used in data mining have their roots in traditional statistical analysis and artificial intelligence work done since the early part of the