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Displaying 1-12 out of 12 total
Improved Multiple Sequence Alignments Using Coupled Pattern Mining
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By K.S.M.Tozammel Hossain,Debprakash Patnaik,Srivatsan Laxman,Prateek Jain,Chris Bailey-Kellogg,Naren Ramakrishnan
Issue Date:September 2013
pp. 1098-1112
We present alignment refinement by mining coupled residues (ARMiCoRe), a novel approach to a classical bioinformatics problem, viz., multiple sequence alignment (MSA) of gene and protein sequences. Aligning multiple biological sequences is a key step in el...
 
Efficient Episode Mining of Dynamic Event Streams
Found in: 2012 IEEE 12th International Conference on Data Mining (ICDM)
By Debprakash Patnaik,Srivatsan Laxman,Badrish Chandramouli,Naren Ramakrishnan
Issue Date:December 2012
pp. 605-614
Discovering frequent episodes over event sequences is an important data mining problem. Existing methods typically require multiple passes over the data, rendering them unsuitable for streaming contexts. We present the first streaming algorithm for mining ...
 
Discovering Excitatory Networks from Discrete Event Streams with Applications to Neuronal Spike Train Analysis
Found in: Data Mining, IEEE International Conference on
By Debprakash Patnaik, Srivatsan Laxman, Naren Ramakrishnan
Issue Date:December 2009
pp. 407-416
Mining temporal network models from discrete event streams is an important problem with applications in computational neuroscience, physical plant diagnostics, and human-computer interaction modeling. We focus in this paper on temporal models representable...
 
Connections between Mining Frequent Itemsets and Learning Generative Models
Found in: Data Mining, IEEE International Conference on
By Srivatsan Laxman, Prasad Naldurg, Raja Sripada, Ramarathnam Venkatesan
Issue Date:October 2007
pp. 571-576
Frequent itemsets mining is a popular framework for pattern discovery. In this framework, given a database of customer transactions, the task is to unearth all patterns in the form of sets of items appearing in a sizable number of transactions. We present ...
 
Discovering Frequent Generalized Episodes When Events Persist for Different Durations
Found in: IEEE Transactions on Knowledge and Data Engineering
By Srivatsan Laxman, P. Sastry, K. Unnikrishnan
Issue Date:September 2007
pp. 1188-1201
This paper is concerned with the framework of frequent episode discovery in event sequences. A new temporal pattern, called the generalized episode, is defined which extends this framework by incorporating event duration constraints explicitly into the pat...
 
Discovering Frequent Episodes and Learning Hidden Markov Models: A Formal Connection
Found in: IEEE Transactions on Knowledge and Data Engineering
By Srivatsan Laxman, P.S. Sastry, K.P. Unnikrishnan
Issue Date:November 2005
pp. 1505-1517
This paper establishes a formal connection between two common, but previously unconnected methods for analyzing data streams: discovering frequent episodes in a computer science framework and learning generative models in a statistics framework. We introdu...
 
Improved Multiple Sequence Alignments Using Coupled Pattern Mining
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Chris Bailey-Kellogg, Debprakash Patnaik, K. S. M. Tozammel Hossain, Naren Ramakrishnan, Prateek Jain, Srivatsan Laxman
Issue Date:September 2013
pp. 1098-1112
We present alignment refinement by mining coupled residues (ARMiCoRe), a novel approach to a classical bioinformatics problem, viz., multiple sequence alignment (MSA) of gene and protein sequences. Aligning multiple biological sequences is a key step in el...
     
An IR-based evaluation framework for web search query segmentation
Found in: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval (SIGIR '12)
By Monojit Choudhury, Niloy Ganguly, Rishiraj Saha Roy, Srivatsan Laxman
Issue Date:August 2012
pp. 881-890
This paper presents the first evaluation framework for Web search query segmentation based directly on IR performance. In the past, segmentation strategies were mainly validated against manual annotations. Our work shows that the goodness of a segmentation...
     
Unsupervised query segmentation using only query logs
Found in: Proceedings of the 20th international conference companion on World wide web (WWW '11)
By Monojit Choudhury, Nikita Mishra, Niloy Ganguly, Rishiraj Saha Roy, Srivatsan Laxman
Issue Date:March 2011
pp. 91-92
We introduce an unsupervised query segmentation scheme that uses query logs as the only resource and can effectively capture the structural units in queries. We believe that Web search queries have a unique syntactic structure which is distinct from that o...
     
Discovering frequent patterns in sensitive data
Found in: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '10)
By Abhradeep Thakurta, Adam Smith, Raghav Bhaskar, Srivatsan Laxman
Issue Date:July 2010
pp. 503-512
Discovering frequent patterns from data is a popular exploratory technique in datamining. However, if the data are sensitive (e.g., patient health records, user behavior records) releasing information about significant patterns or trends carries significan...
     
Stream prediction using a generative model based on frequent episodes in event sequences
Found in: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '08)
By Ryen W. White, Srivatsan Laxman, Vikram Tankasali
Issue Date:August 2008
pp. 5-6
This paper presents a new algorithm for sequence prediction over long categorical event streams. The input to the algorithm is a set of target event types whose occurrences we wish to predict. The algorithm examines windows of events that precede occurrenc...
     
A fast algorithm for finding frequent episodes in event streams
Found in: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '07)
By K. P. Unnikrishnan, P. S. Sastry, Srivatsan Laxman
Issue Date:August 2007
pp. 410-419
Frequent episode discovery is a popular framework for mining data available as a long sequence of events. An episode is essentially a short ordered sequence of event types and the frequency of an episode is some suitable measure of how often the episode oc...
     
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