M. Narasimha Murty

18.0k total citations · 2 hit papers
121 papers, 11.5k citations indexed

About

M. Narasimha Murty is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, M. Narasimha Murty has authored 121 papers receiving a total of 11.5k indexed citations (citations by other indexed papers that have themselves been cited), including 84 papers in Artificial Intelligence, 39 papers in Computer Vision and Pattern Recognition and 26 papers in Information Systems. Recurrent topics in M. Narasimha Murty's work include Face and Expression Recognition (26 papers), Data Mining Algorithms and Applications (21 papers) and Data Management and Algorithms (20 papers). M. Narasimha Murty is often cited by papers focused on Face and Expression Recognition (26 papers), Data Mining Algorithms and Applications (21 papers) and Data Management and Algorithms (20 papers). M. Narasimha Murty collaborates with scholars based in India, United States and Singapore. M. Narasimha Murty's co-authors include Anil K. Jain, Patrick J. Flynn, V. Susheela Devi, G. Phanendra Babu, D.K. Subramanian, Shirish Shevade, S. Asharaf, Sambaran Bandyopadhyay, N. Lokesh and V. S. Ananthanarayana and has published in prestigious journals such as ACM Computing Surveys, Pattern Recognition and Information Sciences.

In The Last Decade

M. Narasimha Murty

113 papers receiving 10.5k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Data clustering

1999 8.7k citations M. Narasimha Murty et al. profile →
Genetic K-means algorithm

1999 1.2k citations M. Narasimha Murty et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
M. Narasimha Murty India	23	6.2k	3.1k	2.0k	2.0k	1.0k	121	11.5k
John Langford United States	37	7.2k 1.2×	6.0k 2.0×	1.3k 0.6×	1.8k 0.9×	1.1k 1.1×	115	15.1k
Hongyuan Zha United States	57	6.2k 1.0×	4.1k 1.3×	3.3k 1.6×	1.4k 0.7×	1.2k 1.2×	325	13.3k
Joydeep Ghosh United States	48	6.9k 1.1×	3.7k 1.2×	1.7k 0.8×	1.6k 0.8×	1.1k 1.1×	308	14.9k
Xiaojin Zhu United States	43	8.0k 1.3×	4.1k 1.3×	1.3k 0.6×	1.2k 0.6×	859 0.8×	151	12.6k
Krzysztof J. Cios United States	29	6.3k 1.0×	3.7k 1.2×	1.3k 0.6×	1.2k 0.6×	572 0.6×	128	14.8k
Ah Chung Tsoi Australia	31	5.1k 0.8×	2.8k 0.9×	913 0.4×	1.2k 0.6×	796 0.8×	184	11.0k
Xiaowei Xu China	19	7.3k 1.2×	3.8k 1.2×	2.8k 1.4×	4.0k 2.0×	1.8k 1.7×	56	17.5k
Andrew Moore United States	44	8.6k 1.4×	2.8k 0.9×	1.4k 0.7×	1.4k 0.7×	2.1k 2.0×	159	16.4k
Peter Flach United Kingdom	45	9.5k 1.5×	6.1k 2.0×	1.9k 1.0×	1.4k 0.7×	1.2k 1.1×	218	20.9k
Olivier Chapelle United States	45	7.7k 1.3×	5.5k 1.8×	2.1k 1.0×	1.1k 0.6×	784 0.8×	89	14.1k

Countries citing papers authored by M. Narasimha Murty

Since Specialization

Citations

This map shows the geographic impact of M. Narasimha Murty's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by M. Narasimha Murty with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Narasimha Murty more than expected).

Fields of papers citing papers by M. Narasimha Murty

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. Narasimha Murty. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by M. Narasimha Murty. The network helps show where M. Narasimha Murty may publish in the future.

Co-authorship network of co-authors of M. Narasimha Murty

This figure shows the co-authorship network connecting the top 25 collaborators of M. Narasimha Murty. A scholar is included among the top collaborators of M. Narasimha Murty based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with M. Narasimha Murty. M. Narasimha Murty is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Bandyopadhyay, Sambaran, et al.. (2020). Outlier Resistant Unsupervised Deep Architectures for Attributed Network Embedding. 25–33. 70 indexed citations

2.

Agrawal, Rakesh, et al.. (2017). Grouping Students for Maximizing Learning from Peers.. Educational Data Mining. 4 indexed citations

3.

Bandyopadhyay, Sambaran & M. Narasimha Murty. (2016). Axioms to characterize efficient incremental clustering. 450–455. 5 indexed citations

4.

Murty, M. Narasimha. (2014). Data Mining Techniques. Current Science. 106(8). 1143–1144. 1 indexed citations

5.

Balamurugan, P., et al.. (2013). Optimizing F-measure with non-convex loss and sparse linear classifiers. 1–8. 9 indexed citations

6.

Murty, M. Narasimha, et al.. (2013). Compression Schemes for Mining Large Datasets: A Machine Learning Perspective. CERN Document Server (European Organization for Nuclear Research). 2 indexed citations

7.

Murty, M. Narasimha, et al.. (2012). On the relation between K-means and PLSA. NOT FOUND REPOSITORY (Indian Institute of Science Bangalore).

8.

Murty, M. Narasimha. (2011). A Generalized Method of Hedonic Prices: Measuring Benefits from Reduced Urban Air Pollution. Digital Library Of The Commons Repository (Indiana University). 6 indexed citations

9.

Chitta, Radha & M. Narasimha Murty. (2009). Two-level k-means clustering algorithm for k–τ relationship establishment and linear-time classification. Pattern Recognition. 43(3). 796–804. 17 indexed citations

10.

Murty, M. Narasimha, et al.. (2008). Clustering Large Symbolic Datasets.

11.

Asharaf, S., et al.. (2007). Core Vector Clustering.. Indian International Conference on Artificial Intelligence. 565–574. 1 indexed citations

12.

Krishnan, Sanjay, et al.. (2007). Focused crawling with scalable ordinal regression solvers. 57–64. 8 indexed citations

13.

Vijaya, P., M. Narasimha Murty, & D.K. Subramanian. (2006). Efficient median based clustering and classification techniques for protein sequences. Pattern Analysis and Applications. 9(2-3). 243–255. 7 indexed citations

14.

Vijaya, P., M. Narasimha Murty, & D.K. Subramanian. (2005). Analysis of Leader based Clustering Algorithms for Pattern Classification.. Indian International Conference on Artificial Intelligence. 2072–2091. 1 indexed citations

15.

Vijaya, P., M. Narasimha Murty, & D.K. Subramanian. (2004). An Efficient Hierarchical Clustering Algorithm for Protein Sequences.. 1. 61–75. 2 indexed citations

16.

Murty, M. Narasimha, et al.. (2001). Bootstrapping for efficient handwritten digit recognition. Pattern Recognition. 34(5). 1047–1056. 12 indexed citations

17.

Ananthanarayana, V. S., M. Narasimha Murty, & D.K. Subramanian. (2001). Efficient clustering of large data sets. Pattern Recognition. 34(12). 2561–2563. 11 indexed citations

18.

Murty, M. Narasimha. (1993). Theta Functions. 1 indexed citations

19.

Shekar, B. H., M. Narasimha Murty, & G. Krishna. (1987). Pattern clustering: an artificial intelligence approach. International Joint Conference on Artificial Intelligence. 214–216.

20.

Shekar, B. H., M. Narasimha Murty, & G. Krishna. (1987). A FRAMEWORK FOR THE SYNTHESIS OF KNOWLEDGE-DIRECTED PATTERN CLASSES. 81–85.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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