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Clustering approaches address segmentation problems. These approaches assign records with a large number of attributes into a relatively small set of groups or "segments." This assignment process is performed automatically by clustering algorithms that identify the distinguishing characteristics of the dataset and then partition the n-dimensional space defined by the dataset attributes along natural cleaving boundaries. There is no need to identify the groupings desired or the attributes that should be used to segment the dataset.
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Clustering is often one of the first steps in data mining analysis. It identifies groups of related records that can be used as a starting point for exploring further relationships. This technique supports the development of population segmentation models, such as demographic-based customer segmentation. Additional analyses using standard analytical and other data mining techniques can determine the characteristics of these segments with respect to some desired outcome. For example, the buying habits of multiple population segments might be compared to determine which segments to target for a new sales campaign.
Clustering divides a database into different groups. The goal of clustering is to find groups that are very different from each other, and whose members are very similar to each other. Unlike classification, you don’t know what the clusters will be when you start, or by which attributes the data will be clustered. Consequently, someone who is knowledgeable in the business must interpret the clusters. Often it is necessary to modify the clustering by excluding variables that have been employed to group instances, because upon examination the user identifies them as irrelevant or not meaningful. After you have found clusters that reasonably segment your database, these clusters may then be used to classify new data. Some of the common algorithms used to perform clustering include Kohonen feature maps and K-means.
Don’t confuse clustering with segmentation. Segmentation refers to the general problem of identifying groups that have common characteristics. Clustering is a way to segment data into groups that are not previously defined, whereas classification is a way to segment data by assigning it to groups that are already defined.
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Check out more information about these books here!
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Clustering is often one of the first steps in data mining analysis. It identifies groups of related records that can be used as a starting point for exploring further relationships. This technique supports the development of population segmentation models, such as demographic-based customer segmentation. Additional analyses using standard analytical and other data mining techniques can determine the characteristics of these segments with respect to some desired outcome. For example, the buying habits of multiple population segments might be compared to determine which segments to target for a new sales campaign.
Clustering divides a database into different groups. The goal of clustering is to find groups that are very different from each other, and whose members are very similar to each other. Unlike classification, you don’t know what the clusters will be when you start, or by which attributes the data will be clustered. Consequently, someone who is knowledgeable in the business must interpret the clusters. Often it is necessary to modify the clustering by excluding variables that have been employed to group instances, because upon examination the user identifies them as irrelevant or not meaningful. After you have found clusters that reasonably segment your database, these clusters may then be used to classify new data. Some of the common algorithms used to perform clustering include Kohonen feature maps and K-means.
Don’t confuse clustering with segmentation. Segmentation refers to the general problem of identifying groups that have common characteristics. Clustering is a way to segment data into groups that are not previously defined, whereas classification is a way to segment data by assigning it to groups that are already defined.