A survey of longest common subsequence algorithms

Bergroth, Lasse; Hakonen, Harri; Raita, T.

doi:10.1109/spire.2000.878178

Cited by 432 publications

(334 citation statements)

References 31 publications

Supporting

Mentioning

329

Contrasting

Unclassified

Order By: Relevance

“…But, this increases the convergence time. In initialization method, each individual is created by choosing a motif length l, obtaining its location through search algorithm and adding some random number α [1,10] to that position to form new positions. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…In the preprocessing phase, the algorithm generates a table which stores details of characters and the number of characters that can be skipped while performing match operation [9]. As the BMH algorithm largely depended on the size of the alphabet and the pattern length, it was further improved by Raita [10] by changing the search procedure of BMH search. The proposed strategy for searching is to compare the last character followed by first character and then the middle character before actually comparing the other characters.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

An Intuitive Wizard to Identify Pattern in Similarity Expressed Gene Set of Rice

Barrow¹,

Naryanan²

2018

IJIES

View full text Add to dashboard Cite

While the genome sequences of different crops have been published and annotated, relatively little is known about the transcriptional networks that regulate gene expression. There are different ways of finding significant motifs in genome sequences, evolutionary motif finding algorithms being one of them. The work presented in this paper uses Differential Evolution (DE) Algorithm to discover new motifs in rice genome sequences and aims to provide an intuitive wizard to analyse the micro-array analysis of expression patterns (motifs) for the queried genes. The used DE algorithm involves crossover operation with fine tuning. Further, to search for already known motifs parallel version of adaptive hash based pattern matching algorithm has been implemented. The study is conducted on rice genome sequences for twelve chromosomes each consisting of approximately 70000 genome sequences. In this paper, experimental results provide a comparison of serial and parallel version of the algorithm applied for the same dataset which shows that parallel implementation improves the performance by almost 25-30% to search the patterns in genome sequences and provide a list of newly discovered significant motifs.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

An Intuitive Wizard to Identify Pattern in Similarity Expressed Gene Set of Rice

Barrow¹,

Naryanan²

2018

IJIES

View full text Add to dashboard Cite

show abstract

“…The length of LCSS of X and Y (or written as LCSS(X, Y )) will be denoted by ζ. The recurrence relation [47] leading to the length of the LCSS for each pair [X(1 . .…”

Section: A Longest Common Sub Sequencementioning

confidence: 99%

“…The sequence of arrows show a possible LCSS path. The LCSS path in this case indicates that there is a match of four elements in the sequence viz., uvuu [31,47]. More details are given in III.…”

Section: B K-meansmentioning

confidence: 99%

New approach to time domain classification of broadband noise in gravitational wave data

Mukherjee¹,

Rizwan²,

Biswas³

2012

Phys. Rev. D

View full text Add to dashboard Cite

Transient broadband noise in gravitational wave (GW) detectors, also known as noise triggers (referred to as triggers for brevity), can often be a deterrant to the efficiency with which astrophysical search pipelines detect sources. It is important to understand their instrumental or environmental origin so that they could be eliminated or accounted for in the data. Since the number of triggers is large, data mining approaches such as clustering and classification are useful tools for this task. Classification of triggers based on a handful of discrete properties has been done in the past. A rich information content is available in the waveform or 'shape' of the triggers that has had a rather restricted exploration so far. This paper presents a new way to classify triggers deriving information from both trigger waveforms as well as their discrete physical properties using a sequential combination of the Longest Common Sub-Sequence (LCSS) and LCSS coupled with Fast Time Series Evaluation (FTSE) for waveform classification and the multidimensional hierarchical classification (MHC) analysis for the grouping based on physical properties. A generalized k-means algorithm is used with the LCSS (and LCSS+FTSE) for clustering the triggers using a validity measure to determine the correct number of clusters in absence of any prior knowledge. The results have been demonstrated by simulations and by application to a segment of real LIGO data from the sixth science run.

show abstract

“…Dynamic programming enables us to solve such problems in polynomial time. For example, the longest common subsequence problem, which requires finding the longest common subsequence of given two sequences, can be solved by the dynamic programming approach [2]. Since a sequence have an exponential number of subsequences, a straightforward algorithm takes an exponential time to find the longest common subsequence.…”

Section: Introductionmentioning

confidence: 99%

A GPU Implementation of Dynamic Programming for the Optimal Polygon Triangulation

Ito

Nakano

2013

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYThis paper presents a GPU (Graphics Processing Units) implementation of dynamic programming for the optimal polygon triangulation. Recently, GPUs can be used for general purpose parallel computation. Users can develop parallel programs running on GPUs using programming architecture called CUDA (Compute Unified Device Architecture) provided by NVIDIA. The optimal polygon triangulation problem for a convex polygon is an optimization problem to find a triangulation with minimum total weight. It is known that this problem for a convex n-gon can be solved using the dynamic programming technique in O(n 3 ) time using a work space of size O(n 2 ). In this paper, we propose an efficient parallel implementation of this O(n 3 )-time algorithm on the GPU. In our implementation, we have used two new ideas to accelerate the dynamic programming. The first idea (adaptive granularity) is to partition the dynamic programming algorithm into many sequential kernel calls of CUDA, and to select the best parameters for the size and the number of blocks for each kernel call. The second idea (sliding and mirroring arrangements) is to arrange the working data for coalesced access of the global memory in the GPU to minimize the memory access overhead. Our implementation using these two ideas solves the optimal polygon triangulation problem for a convex 8192-gon in 5.57 seconds on the NVIDIA GeForce GTX 680, while a conventional CPU implementation runs in 1939.02 seconds. Thus, our GPU implementation attains a speedup factor of 348.02.

show abstract

A survey of longest common subsequence algorithms

Cited by 432 publications

References 31 publications

An Intuitive Wizard to Identify Pattern in Similarity Expressed Gene Set of Rice

An Intuitive Wizard to Identify Pattern in Similarity Expressed Gene Set of Rice

New approach to time domain classification of broadband noise in gravitational wave data

A GPU Implementation of Dynamic Programming for the Optimal Polygon Triangulation

Contact Info

Product

Resources

About