Libraries at University of Nebraska-Lincoln

 

Date of this Version

Fall 9-16-2019

Citation

Lu, Z. (Ed.). (2013). Information retrieval methods for multidisciplinary applications. IGI Global.

Swamy, B. K., & Kulkarni, P. A. (2006). Intelligent decision making based on pattern matching & mind-maps. In Proceedings of the 10th WSEAS international conference on Computers (pp. 492-497). World Scientific and Engineering Academy and Society (WSEAS).

Sarmiento, A. M., & Nagi, R. (1999). A review of integrated analysis of production-distribution systems. IIE transactions, 31(11), 1061-1074.

Gaikwad, S. M., Joshi, R. R., & Mulay, P. (2015). Cluster Mapping with the help of New Extended MCF Algorithm and MCF Algorithm to Recommend an Ice Cream to the Diabetic Patient. METHODOLOGY, 1(6), 7.

Degadwala, S. D., Mahajan, A. D., & Vyas, D. J. Privacy Preservation using T-Closeness with Numerical Attributes.

Joshi, P. M., & Kulkarni, P. A. (2011). A novel approach for clustering based on pattern analysis. International Journal of Computer Applications, 975, 8887.

Mulay, P., Patel, K., & Gauchia, H. G. (2017). Distributed System Implementation Based on “Ants Feeding Birds” Algorithm: Electronics Transformation via Animals and Human. In Detecting and Mitigating Robotic Cyber Security Risks (pp. 51-85). IGI Global.

Mulay, P., Joshi, R. R., Anguria, A. K., Gonsalves, A., Deepankar, D., & Ghosh, D. (2017). Threshold Based Clustering Algorithm Analyzes Diabetic Mellitus. In Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications (pp. 27-33). Springer, Singapore.

Mulay, P., & Kulkarni, P. A. (2013). Knowledge augmentation via incremental clustering: new technology for effective knowledge management. International Journal of Business Information Systems, 12(1), 68-87.

Shinde, K., & Mulay, P. (2017, April). Cbica: Correlation based incremental clustering algorithm, a new approach. In Convergence in Technology (I2CT), 2017 2nd International Conference for (pp. 291-296). IEEE.

Lakshmanaprabu, S. K., Shankar, K., Gupta, D., Khanna, A., Rodrigues, J. J., Pinheiro, P. R., & de Albuquerque, V. H. C. (2018). Ranking analysis for online customer reviews of products using opinion mining with clustering. Complexity, 2018.

Lakshmanaprabu, S. K., Shankar, K., Khanna, A., Gupta, D., Rodrigues, J. J., Pinheiro, P. R., & De Albuquerque, V. H. C. (2018). Effective Features to Classify Big Data Using Social Internet of Things. IEEE Access, 6, 24196-24204.

Kulkarni, P. A., & Mulay, P. (2013). Evolve systems using incremental clustering approach. Evolving Systems, 4(2), 71-85.

Kulkarni, P. A., Dwivedi, S., & Haribhakta, Y. V. (2015). U.S. Patent Application No. 14/676,680.

Kadam, S., Bandyopadhyay, P. K., & Patil, Y. (2016). Mapping the field through bibliometric analysis of passenger centric railway transportation. International Journal of Automation and Logistics, 2(4), 349-368.

Kulkarni, H. (2017, September). Intelligent context based prediction using probabilistic intent-action ontology and tone matching algorithm. In Advances in Computing, Communications and Informatics (ICACCI), 2017 International Conference on (pp. 656-662). IEEE.

Kulkarni, A., Tokekar, V., & Kulkarni, P. (2015). Discovering context of labeled text documents using context similarity coefficient. Procedia computer science, 49, 118-127.

Kumar, A., Ahuja, H., Singh, N. K., Gupta, D., Khanna, A., & Rodrigues, J. J. (2018). Supported matrix factorization using distributed representations for personalised recommendations on twitter. Computers & Electrical Engineering, 71, 569-577.

Joshi, R. R., & Mulay, P. (2018). Deep Incremental Statistical Closeness Factor Based Algorithm (DIS-CFBA) to assess Diabetes Mellitus. BLOOD, 115, 210.

Johnson, T., & Singh, S. K. (2016). Quantitative Performance Analysis for the Family of Enhanced Strange Points Clustering Algorithms. International Journal of Applied Engineering Research, 11(9), 6872-6880.

Gaikwad, S. M., Joshi, R. R., & Mulay, P. (2016). System dynamics modeling for analyzing recovery rate of diabetic patients by mapping sugar content in ice cream and sugar intake for the day. In Proceedings of the Second International Conference on Computer and Communication Technologies (pp. 743-749). Springer, New Delhi.

Archana Chaudhari, & Mulay, P. (2018). SCSI: Real-time Data Analysis with Casandra and Spark. Big Data Processing Using Spark in Cloud (Vol. 43). Studies in Big Data Springer.

Abstract

For clustering accuracy, on influx of data, the parameter-free incremental clustering research is essential. The sole purpose of this bibliometric analysis is to understand the reach and utility of incremental clustering algorithms. This paper shows incremental clustering for time series dataset was first explored in 2000 and continued thereafter till date. This Bibliometric analysis is done using Scopus, Google Scholar, Research Gate, and the tools like Gephi, Table2Net, and GPS Visualizer etc. The survey revealed that maximum publications of incremental clustering algorithms are from conference and journals, affiliated to Computer Science, Chinese lead publications followed by India then United States. Convergence optimality is another prominent keyword and less attentiveness towards correlation has observed. For betweenness and friendly measures keywords, after physics and astronomy; engineering is the contributing subject area, minimal contribution of review papers are observed in this art-search. The effectual incremental learning is feasible via parameter-free incremental clustering algorithm, applicable to all domains and hence this study.

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