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20
Matching for RunLength Encoded Strings
, 1999
"... this paper, we develop significantly faster algorithms for a special class of strings which emerge frequently in pattern matching problems. A string S is runlength encoded if it is described as an ordered sequence of pairs (oe; i), each consisting of an alphabet symbol oe and an integer i. Each pai ..."
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Cited by 24 (2 self)
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this paper, we develop significantly faster algorithms for a special class of strings which emerge frequently in pattern matching problems. A string S is runlength encoded if it is described as an ordered sequence of pairs (oe; i), each consisting of an alphabet symbol oe and an integer i. Each pair corresponds to a run in S consisting of i consecutive occurrences of oe. For example, the string
Longest common subsequence from fragments via sparse dynamic programming
 Proc. 6th Eur. Symp. Algorithm
, 1998
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Looking for monotonicity properties of a similarity constraint on sequences
 In ACM Symposium of Applied Computing SAC’2006, Special Track on Data Mining
, 2006
"... Constraintbased mining techniques on sequence databases have been studied extensively the last few years and efficient algorithms enable to compute complete collections of patterns (e.g., sequences) which satisfy conjunctions of monotonic and/or antimonotonic constraints. Studying new applications ..."
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Cited by 9 (4 self)
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Constraintbased mining techniques on sequence databases have been studied extensively the last few years and efficient algorithms enable to compute complete collections of patterns (e.g., sequences) which satisfy conjunctions of monotonic and/or antimonotonic constraints. Studying new applications of these techniques, we believe that a primitive constraint which enforces enough similarity w.r.t a given reference sequence would be extremely useful and should benefit from such a recent algorithmic breakthrough. A non trivial similarity constraint is however neither monotonic nor antimonotonic. Therefore, we have studied its definition as a conjunction of two constraints which satisfy the desired monotonicity properties: a pattern is called similar to a reference pattern x when its longest common subsequence with x (LCS) is large enough (i.e., a monotonic part) and when the number of deletions such that it becomes the LCS is small enough (i.e., an antimonotonic part). We provide an experimental validation which confirms the added value of this approach on a biological database. Classical issues like scalability and pruning efficiency are discussed. 1.
Aggregation of composite solutions: strategies, models, examples. Electronic preprint
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ReUse Dynamic Programming for Sequence Alignment: An Algorithmic Toolkit
 STRING ALGORITHMICS, UNITED KINGDOM
, 2005
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String Pattern Matching For A Deluge Survival Kit
, 2000
"... String Pattern Matching concerns itself with algorithmic and combinatorial issues related to matching and searching on linearly arranged sequences of symbols, arguably the simplest possible discrete structures. As unprecedented volumes of sequence data are amassed, disseminated and shared at an incr ..."
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Cited by 6 (1 self)
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String Pattern Matching concerns itself with algorithmic and combinatorial issues related to matching and searching on linearly arranged sequences of symbols, arguably the simplest possible discrete structures. As unprecedented volumes of sequence data are amassed, disseminated and shared at an increasing pace, effective access to, and manipulation of such data depend crucially on the efficiency with which strings are structured, compressed, transmitted, stored, searched and retrieved. This paper samples from this perspective, and with the authors' own bias, a rich arsenal of ideas and techniques developed in more than three decades of history.
Introduction Introducing Softness into Inductive Queries on String Databases
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Fully incremental LCS computation
 In Proceedings of FCT
, 2005
"... Abstract. Sequence comparison is a fundamental task in pattern matching. Its applications include file comparison, spelling correction, information retrieval, and computing (dis)similarities between biological sequences. A common scheme for sequence comparison is the longest common subsequence (LCS) ..."
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Cited by 2 (0 self)
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Abstract. Sequence comparison is a fundamental task in pattern matching. Its applications include file comparison, spelling correction, information retrieval, and computing (dis)similarities between biological sequences. A common scheme for sequence comparison is the longest common subsequence (LCS) metric. This paper considers the fully incremental LCS computation problem as follows: For any strings A, B and characters a, b, compute LCS(aA, B), LCS(A, bB), LCS(Aa, B), and LCS(A, Bb), provided that L = LCS(A, B) is already computed. We present an efficient algorithm that computes the four LCS values above, in O(L) orO(n) time depending on where a new character is added, where n is the length of A. Our algorithm is superior in both time and space complexities to the previous known methods. 1
Mustapha N.,” A Recommender System Approach for Classifying User Navigation Patterns Using Longest Common Subsequence Algorithm
 American Journal of Scientific Research ISSN 1450223X Issue
"... Prediction of user future movements and intentions based on the users ’ clickstream data is a main challenging problem in Web based recommendation systems. Web usage mining based on the users ’ clickstream data has become the subject of exhaustive research, as its potential for web based personalize ..."
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Prediction of user future movements and intentions based on the users ’ clickstream data is a main challenging problem in Web based recommendation systems. Web usage mining based on the users ’ clickstream data has become the subject of exhaustive research, as its potential for web based personalized services, predicting user near future intentions, adaptive Web sites and customer profiling is recognized. A variety of the recommender systems for online personalization through web usage mining have been proposed. However, the quality of the recommendations in the current systems to predict users ’ future intentions systems cannot still satisfy users in the particular huge web sites. In this paper, to provide online predicting effectively, we develop a model for online predicting through web usage mining system and propose a novel approach for classifying user navigation patterns to predict users ’ future intentions. The approach is based on the using longest common subsequence algorithm to classify current user activities to predict user next movement. We have tested our proposed model on the CTI datasets. The results indicate that the approach can improve the quality of the system for the predictions. A Recommender System Approach for Classifying User Navigation
String comparison by transposition networks
, 903
"... Abstract. Computing string or sequence alignments is a classical method of comparing strings and has applications in many areas of computing, such as signal processing and bioinformatics. Semilocal string alignment is a recent generalisation of this method, in which the alignment of a given string ..."
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Abstract. Computing string or sequence alignments is a classical method of comparing strings and has applications in many areas of computing, such as signal processing and bioinformatics. Semilocal string alignment is a recent generalisation of this method, in which the alignment of a given string and all substrings of another string are computed simultaneously at no additional asymptotic cost. In this paper, we show that there is a close connection between semilocal string alignment and a certain class of traditional comparison networks known as transposition networks. The transposition network approach can be used to represent different string comparison algorithms in a unified form, and in some cases provides generalisations or improvements on existing algorithms. This approach allows us to obtain new algorithms for sparse semilocal string comparison and for comparison of highly similar and highly dissimilar strings, as well as of runlength compressed strings. We conclude that the transposition network method is a very general and flexible way of understanding and improving different string comparison algorithms, as well as their efficient implementation. 1