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 activity. Although the information is based on past performance, it can be an excellent indicator of customer behavior and trends.
Two excellent examples of data mining algorithms are the clustering and nearest neighbor predictors. 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 sorting the laundry is functioning as the algorithm. He or she separates the laundry into piles by attributes: colors, dry cleaning, and whites are all separated.
The actual decision making process involved in this activity is the details of the algorithm. First, the data set must be limited to items relevant to the exercise. Shoes are not included in laundry sorting, although they may be in the same physical space. The decision must be made in advance about what characteristics will be used to separate the laundry and the size of each pile.
Nearest neighbor predictor is based on the identification of closely matching examples. The criteria must be provided in the initial stages, specifying what the item or data is and what the definition of nearest will include. This type of algorithm follows a similar pattern to logical thought process.
The primary benefit of data mining algorithms is the ability of the program to create and identify patterns within a huge volume of data. The ability to identify neighbors in a particular setting is easy to do in a small group. However, data collected from all the sales transactions completed within the year or in a district requires special programs and logic to do with any accuracy.
People who can create data mining algorithms to meet users needs work in business intelligence or data mining. This is a very complex expansion of statistics growing in popularity as organizations seek to yield 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 in behavior, and use this information to predict future actions. This information is very valuable for business, organizations, and governments.