Introducing the Microsoft Sequence Clustering Algorithm - Data Mining

As the name suggests, the Microsoft Sequence Clustering algorithm is a hybrid of sequence and clustering techniques. It is designed to analyze a population of cases that contains sequence data and group those cases into more or less homogeneous segments based on the similarity of those sequences.

A sequence is a series of discrete events (states). Usually the number of discrete states in a sequence is finite. Sequence data is ubiquitous in the real world. Lots of information is encoded in sequence form. For example, a DNA sequence is a series of four discrete states: A(adenosine), G (guanine), C (cytosine), and T (thymidine). The list of courses a student takes at a university forms a sequence. The series of URL clicks of a Web user is a sequence. In a shopping basket example, if we don’t care about the order of the product purchases, the business problem of market basket analysis is an association task. If we do care about the order of the product purchases, the purchase data forms a sequence, and this problem is a sequence task. Figure displays a weather forecast sequence.

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