Data mining algorithms are programs used to find patterns in data sets, primarily to determine customer needs. Clustering and closest neighbor predictors are examples. The algorithms require decision-making and can identify patterns within large data volumes. Developers in business intelligence or data mining create algorithms to predict future actions, providing valuable information to organizations.
Data mining algorithms are programmed queries and programs used to identify patterns and trends in data sets. The primary use of data mining is to determine customer needs and preferences, based on their actual business. While the information is based on past performance, it can be an excellent indicator of customer behavior and trends.
Two great examples of data mining algorithms are clustering predictors and closest. Clustering is a term used to describe an activity where individual units or data share important attributes. Separating the laundry is a logical example of this behavior. The person ordering the laundry works like an algorithm. He or she separates the fleece laundry by attributes: colors, dry cleaning, and whites are all separate.
The actual decision making involved in this activity is the details of the algorithm. First, the data set must be limited to the items relevant to the exercise. Shoes are not included in the laundry sorting, although they may be in the same physical space. The decision must be made in advance as to which features will be used to separate the laundry and the size of each pile.
The closest neighbor predictor is based on identifying closely matching examples. Criteria should be provided in the initial stages, specifying what the item or data is and what the definition of closest will include. This type of algorithm follows a pattern similar to the logical thinking process.
The main advantage of data mining algorithms is the program’s ability to create and identify patterns within a huge volume of data. The ability to identify neighbors in a particular environment is easy to do in a small group. However, data collected from all sales transactions completed within the year or in a district requires special programming and logic to be accurate.
People who can create data mining algorithms to meet user needs work in business intelligence or data mining. This is a very complex expansion of statistics that is growing in popularity as organizations seek to get a more tangible return from the data they have collected. An efficient developer can create a set of data mining algorithms that accurately identify patterns of behavior and use this information to predict future actions. This information is very valuable to companies, organizations and governments.
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