Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic segmentation of text into structured records
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
On effective classification of strings with wavelets
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
HIDDEN MARKOV MODELS IN COMPUTATIONAL BIOLOGY: APPLICATIONS TO PROTEIN MODELING
HIDDEN MARKOV MODELS IN COMPUTATIONAL BIOLOGY: APPLICATIONS TO PROTEIN MODELING
PROTEIN MODELING USING HIDDEN MARKOV MODELS: ANALYSIS OF GLOBINS
PROTEIN MODELING USING HIDDEN MARKOV MODELS: ANALYSIS OF GLOBINS
On demand classification of data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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String data has recently become important because of its use in a number of applications such as computational and molecular biology, protein analysis, and market basket data. In many cases, these strings contain a wide variety of substructures which may have physical significance for that application. For example, such substructures could represent important fragments of a DNA string or an interesting portion of a fraudulent transaction. In such a case, it is desirable to determine the identity, location, and extent of that substructure in the data. This is a much more difficult generalization of the classification problem, since the latter problem labels entire strings rather than deal with the more complex task of determining string fragments with a particular kind of behavior. The problem becomes even more complicated when different kinds of substrings show complicated nesting patterns. Therefore, we define a somewhat different problem which we refer to as the generalized classification problem. We propose a scalable approach based on hidden markov models for this problem. We show how to implement the generalized string classification procedure for very large data bases and data streams. We present experimental results over a number of large data sets and data streams.