Biclustering is a data mining technique that sorts information in a matrix by assigning rows and columns simultaneously, improving efficiency. It allows computers to identify related information and patterns, and there are various algorithms available.
Biclustering is a data mining technique that sorts information in a matrix by assigning the rows and columns of the matrix simultaneously. At the heart of this technique is efficiency, which allows the computer to sift through and sort a large amount of data in a shorter time than single clustering methods. Biclustering is simply a general title of a particular class of data mining techniques; there are many different algorithms that can fall into this category, including block clustering, the plaid model, coupled bidirectional clustering, and interrelated bidirectional clustering.
To understand the importance of biclustering, you must first understand the general concept of data mining. Data mining is taking a large pile of data, such as information downloaded from a company’s main database, and sorting it to identify trends and other useful patterns. This type of analysis can be used to determine patterns that might not otherwise be apparent through random study, such as consumer buying trends and stock market fluctuations. Data mining can be conducted manually by a human analyst or electronically using some type of data mining algorithm; this is where biclustering comes into play.
During the data mining process, the computer conducting the analysis will attempt to sort related information together. This process is known as “bundling”. Clustering allows the computer to flex its artificial intelligence by recognizing when two or more pieces of information are related to each other, putting them together in an array. Normally the rows or columns of the matrix are filled, but only one at a time.
Biclustering eliminates this limitation by allowing the computer to pad both rows and columns at the same time. This improves the efficiency of the clustering process, but can lead to arrays arranged differently depending on the particular algorithm used. For example, a computer arranging things with constant match values in rows versus a computer arranging things with constant match values in columns will generate different looking arrays using exactly the same values. There is no “right” way to group data; it all depends on the particular situation and preferences of the individual conducting the data mining.
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