Karam Gouda

According to our database1, Karam Gouda authored at least 16 papers between 1998 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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Links

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Bibliography

2022
Scaling Subgraph Matching by Improving Ullmann Algorithm.
Comput. Informatics, 2022

2019
A novel edge-centric approach for graph edit similarity computation.
Inf. Syst., 2019

New Subgraph Isomorphism Algorithms: Vertex versus Path-at-a-time Matching.
CoRR, 2019

2018
A novel hierarchical-based framework for upper bound computation of graph edit distance.
Pattern Recognit., 2018

2017
Efficient String Edit Similarity Join Algorithm.
Comput. Informatics, 2017

2016
BFST_ED: A Novel Upper Bound Computation Framework for the Graph Edit Distance.
Proceedings of the Similarity Search and Applications - 9th International Conference, 2016

CSI_GED: An efficient approach for graph edit similarity computation.
Proceedings of the 32nd IEEE International Conference on Data Engineering, 2016

2015
An improved global lower bound for graph edit similarity search.
Pattern Recognit. Lett., 2015

2013
Efficiently Using Prime-Encoding for Mining Frequent Itemsets in Sparse Data.
Comput. Informatics, 2013

Compressed feature-based filtering and verification approach for subgraph search.
Proceedings of the Joint 2013 EDBT/ICDT Conferences, 2013

2010
Prism: An effective approach for frequent sequence mining via prime-block encoding.
J. Comput. Syst. Sci., 2010

2007
Prism: A Primal-Encoding Approach for Frequent Sequence Mining.
Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), 2007

2005
GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets.
Data Min. Knowl. Discov., 2005

2003
Fast vertical mining using diffsets.
Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24, 2003

2001
Efficiently Mining Maximal Frequent Itemsets.
Proceedings of the 2001 IEEE International Conference on Data Mining, 29 November, 2001

1998
Using relevant reasoning to solve the relevancy problem in knowledge discovery in databases.
Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 1998


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