In this article, you are going to learn about What is Data Mining? , how does it work?.
A data warehouse is regarded as a type of mine, where the data is stored. Data Mining is the process of selecting, exploring, and modeling large amounts of data to discover previously unknown relationships that can support decision making.
Data Mining software searches through large amounts of data for meaningful patterns of information. Data Mining, the extraction of hidden predictive information from a large database, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouse. Data Mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions.
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The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of a decision support system. Data Mining tools can answer business questions that traditionally were time-consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.
How Does Data Mining Work??
Data Mining is concerned with the analysis of data and the use of software techniques for
finding hidden patterns and trends in historical business activity. This analysis can be used to help
managers make decisions about strategic changes in business operations to gain competitive advantages in the marketplace.
Data Mining can discover new correlations, patterns, and trends in vast amounts of business data
stored in the data warehouse. The data mining software uses advanced pattern recognition algorithms, as well as a variety of mathematical and statistical techniques, to sift through mountains of data to extract previously unknown strategic business information. Many organizations & companies use data mining for the following purpose:
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-Perform market-based analysis to identify new product bundles.
-Find root causes of quality or manufacturing problems.
-Prevent customer attrition and acquire new customers.
-Cross-sells the existing customers.
-Profile customers with more accuracy.
Data mining analysis tends to work from the data up and the best techniques are those developed with an orientation towards large volumes of data, making use of as much of the collected data as possible to arrive at reliable conclusions and decisions.
The analysis process starts with a set of data, uses a methodology to develop an optimal representation of the structure of the data during which time knowledge is acquired. Once knowledge has been acquired this can be extended to larger sets of data working on the assumption that the larger data set has a structure similar to the sample data.
Stages In Data Mining:
The phase depicted start with the raw data and finish with the extracted knowledge which was acquired as a result of the following stages:
Selecting or segmenting the data according to some criteria e.g. all those people who own a car, in this way subsets of the data can be determined.
This is the data cleansing stage where certain information is removed which is deemed unnecessary and may slow down queries for example unnecessary to note the sex of a patient when studying pregnancy. Also, the data is reconfigured to ensure a consistent format as there is a possibility of inconsistent formats because the data is driven from several sources e.g.sex may record as a form and also as 1or 0.
The data is not merely transferred across the transformed in that overlays may be added such as the demographic overlays commonly used in market research. The data is made useable and navigable.
This stage is concerned with the extraction of patterns from the data. A pattern can be defined as given a set of facts F, a language L, and some measure of certainty C,a pattern is a statement S in L that describes relationships among subsets Fs of F with a certainty c such as S is simpler in some sense than the enumeration of all the facts in Fs.
-Interpretation and evaluation:
The patterns identified by the system are interpreted into knowledge which can then be used to support human decision -making e.g .prediction and classification tasks, summarizing the contents of a database, or explaining observed phenomena.
Use Of Data Mining;
Some data mining tools are complex statistical analysis applications, and others use additional tools which go beyond statistical analysis and hypothesis testing. While some tools help find predefined relationships and ratios, other techniques are also used in data mining, including artificial intelligence techniques in decision support and expert systems.
To illustrate the difference between traditional queries and data-mining queries, consider the following examples. A typical traditional query would be-” What is the relationship between the amount of product A and Product B that we sold over the past quarter?”
A typical data mining query would be: ” discover two products most likely to sell together on a weekend.” The latter query lets the software find patterns that would otherwise not be detected through observations.
While data has traditionally been used to see whether this or that pattern exits, data mining allows you to ask what pattern exits. Thus, some experts say that in data mining you let the computer answer questions that you do not know to ask. The combination of data warehousing techniques and data mining software makes it easier to predict future outcomes based on patterns discovered within historical data.