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Semantic ScholarRaj P. Gopalan
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2010 – today
- 2018
[j5]Suneetha Uppu, Aneesh Krishna, Raj P. Gopalan:
A Review on Methods for Detecting SNP Interactions in High-Dimensional Genomic Data. IEEE/ACM Trans. Comput. Biology Bioinform. 15(2): 599-612 (2018)- 2016
- [j4]Tomohiro Inoue, Aneesh Krishna, Raj P. Gopalan:
Approximate Query Processing on High Dimensionality Database Tables Using Multidimensional Cluster Sampling View. JSW 11(1): 80-93 (2016) - [j3]Suneetha Uppu, Aneesh Krishna, Raj P. Gopalan:
A Deep Learning Approach to Detect SNP Interactions. JSW 11(10): 965-975 (2016) - [c29]Suneetha Uppu, Aneesh Krishna, Raj P. Gopalan:
Towards Deep Learning in genome-Wide Association Interaction studies. PACIS 2016: 20 - 2015
- [j2]Suneetha Uppu, Aneesh Krishna, Raj P. Gopalan:
Rule-based analysis for detecting epistasis using associative classification mining. NetMAHIB 4(1): 12 (2015) - [c28]Tomohiro Inoue, Aneesh Krishna, Raj P. Gopalan:
Multidimensional Cluster Sampling View on Large Databases for Approximate Query Processing. EDOC 2015: 104-111 - [c27]Suneetha Uppu, Aneesh Krishna, Raj P. Gopalan:
A Multifactor Dimensionality Reduction Based Associative Classification for Detecting SNP Interactions. ICONIP (1) 2015: 328-336 - [c26]Chitra M. Subramanian, Aneesh Krishna, Arshinder Kaur, Raj P. Gopalan:
Quantitative Reasoning of Goal Satisfaction in the i*Framework. SEKE 2015: 666-669 - 2014
- [j1]Suneetha Uppu, Aneesh Krishna, Raj P. Gopalan:
Detecting SNP Interactions in Balanced and Imbalanced Datasets using Associative Classification. Austr. J. Intelligent Information Processing Systems 14(1) (2014) - [c25]Raj P. Gopalan, Aneesh Krishna:
Duplicate Bug Report Detection Using Clustering. Australian Software Engineering Conference 2014: 104-109 - [c24]Suneetha Uppu, Aneesh Krishna, Raj P. Gopalan:
An Associative Classification Based Approach for Detecting SNP-SNP Interactions in High Dimensional Genome. BIBE 2014: 329-333 - 2013
- [c23]Amit Rudra, Raj P. Gopalan, N. R. Achuthan:
Selecting Adequate Samples for Approximate Decision Support Queries. ICEIS (1) 2013: 46-55 - [c22]Amit Rudra, Raj P. Gopalan, N. R. Achuthan:
Estimating Sufficient Sample Sizes for Approximate Decision Support Queries. ICEIS 2013: 85-99 - 2012
- [c21]Amit Rudra, Raj P. Gopalan, Narasimaha Achuthan:
An Efficient Sampling Scheme for Approximate Processing of Decision Support Queries. ICEIS (1) 2012: 16-26
2000 – 2009
- 2008
- [c20]Alva Erwin, Raj P. Gopalan, N. R. Achuthan:
Efficient Mining of High Utility Itemsets from Large Datasets. PAKDD 2008: 554-561 - 2007
- [c19]Alva Erwin, Raj P. Gopalan, N. R. Achuthan:
CTU-Mine: An Efficient High Utility Itemset Mining Algorithm Using the Pattern Growth Approach. CIT 2007: 71-76 - [c18]Alva Erwin, Raj P. Gopalan, N. R. Achuthan:
A Bottom-Up Projection Based Algorithm for Mining High Utility Itemsets. AIDM 2007: 3-10 - 2006
- [c17]Yanrong Li, Raj P. Gopalan:
Clustering Transactional Data Streams. Australian Conference on Artificial Intelligence 2006: 1069-1073 - [c16]Yanrong Li, Raj P. Gopalan:
Clustering high dimensional sparse transactional data with constraints. GrC 2006: 692-695 - [p1]N. R. Achuthan, Raj P. Gopalan, Amit Rudra:
Mining Value-Based Item Packages - An Integer Programming Approach. Selected Papers from AusDM 2006: 78-89 - 2005
- [c15]
- 2004
- [c14]Yanrong Li, Raj P. Gopalan:
Effective Sampling for Mining Association Rules. Australian Conference on Artificial Intelligence 2004: 391-401 - [c13]Yudho Giri Sucahyo, Raj P. Gopalan:
Building a More Accurate Classifier Based on Strong Frequent Patterns. Australian Conference on Artificial Intelligence 2004: 1036-1042 - [c12]Yudho Giri Sucahyo, Raj P. Gopalan:
CT-PRO: A Bottom-Up Non Recursive Frequent Itemset Mining Algorithm Using Compressed FP-Tree Data Structure. FIMI 2004 - 2003
- [c11]Yudho Giri Sucahyo, Raj P. Gopalan:
CT-ITL : Efficient Frequent Item Set Mining Using a Compressed Prefix Tree with Pattern Growth. ADC 2003: 95-104 - [c10]Raj P. Gopalan, Yudho Giri Sucahyo:
Fast Frequent Itemset Mining using Compressed Data Representation. Applied Informatics 2003: 1203-1208 - [c9]Yudho Giri Sucahyo, Raj P. Gopalan, Amit Rudra:
Efficiently Mining Frequent Patterns from Dense Datasets Using a Cluster of Computers. Australian Conference on Artificial Intelligence 2003: 233-244 - [c8]Raj P. Gopalan, Yudho Giri Sucahyo:
Efficient Mining of Long Frequent Patterns from Very Large Dense Datasets. HIS 2003: 652-661 - [c7]Raj P. Gopalan, Yohanes Hendra:
Retrieval characteristics of an untrained holographic index for image databases. ICIP (2) 2003: 555-558 - [c6]Raj P. Gopalan, Yudho Giri Sucahyo:
Improving the Efficiency of Frequent Pattern Mining by Compact Data Structure Design. IDEAL 2003: 576-583 - 2002
- [c5]Raj P. Gopalan, Grant Lee:
Indexing of Image Databases Using Untrained 4D Holographic Memory Model. Australian Joint Conference on Artificial Intelligence 2002: 237-248 - [c4]Raj P. Gopalan, Yudho Giri Sucahyo:
TreeITL-Mine: Mining Frequent Itemsets Using Pattern Growth, Tid Intersection, and Prefix Tree. Australian Joint Conference on Artificial Intelligence 2002: 535-546 - [c3]Raj P. Gopalan, Tariq Nuruddin, Yudho Giri Sucahyo:
Algebraic specification of association rule queries. Data Mining and Knowledge Discovery: Theory, Tools, and Technology 2002: 77-87 - [c2]Raj P. Gopalan, Yudho Giri Sucahyo:
ITL-MINE: Mining Frequent Itemsets More Efficiently. FSKD 2002: 167- - 2001
- [i1]Raj P. Gopalan, Tariq Nuruddin, Yudho Giri Sucahyo:
A Seamless Integration of Association Rule Mining with Database Systems. CoRR cs.DB/0106055 (2001) - 2000
- [c1]Amit Rudra, Raj P. Gopalan:
Adaptive Use of a Cluster of PCs for Data Warehousing Applications: Some Problems and Issues. SAC (2) 2000: 698-703
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