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- Radha Chitta, Rong Jin, Timothy C. Havens, Anil K. Jain
- KDD
- 2011

Digital data explosion mandates the development of scalable tools to organize the data in a meaningful and easily accessible form. Clustering is a commonly used tool for data organization. However, many clustering algorithms designed to handle large data sets assume linear separability of data and hence do not perform well on real world data sets. While… (More)

- Radha Chitta, Rong Jin, Anil K. Jain
- 2012 IEEE 12th International Conference on Data…
- 2012

Kernel clustering algorithms have the ability to capture the non-linear structure inherent in many real world data sets and thereby, achieve better clustering performance than Euclidean distance based clustering algorithms. However, their quadratic computational complexity renders them non-scalable to large data sets. In this paper, we employ random Fourier… (More)

- Radha Chitta, M. Narasimha Murty
- Pattern Recognition
- 2010

- Radha Chitta, Rong Jin, Timothy C. Havens, Anil K. Jain
- ArXiv
- 2014

Kernel-based clustering algorithms have the ability to capture the non-linear structure in real world data. Among various kernel-based clustering algorithms, kernel k -means has gained popularity due to its simple iterative nature and ease of implementation. However, its run-time complexity and memory footprint increase quadratically in terms of the size of… (More)

- Timothy C. Havens, Radha Chitta, Anil K. Jain, Rong Jin
- FUZZ-IEEE
- 2011

The ubiquity of personal computing technology has produced an abundance of staggeringly large data sets—the Library of Congress has stored over 160 terabytes of web data and it is estimated that Facebook alone logs over 25 terabytes of data per day. There is a great need for systems by which one can elucidate the similarity and dissimilarity among and… (More)

- Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, Jing Gao
- KDD
- 2017

Predicting the future health information of patients from the historical Electronic Health Records (EHR) is a core research task in the development of personalized healthcare. Patient EHR data consist of sequences of visits over time, where each visit contains multiple medical codes, including diagnosis, medication, and procedure codes. The most important… (More)

- Anil Jain, Rong Jin, Radha Chitta
- 2014

Clustering is an unsupervised learning problem whose objective is to find a partition of the given data. However, a major challenge in clustering is to define an appropriate objective function in order to to find an optimal partition that is useful to the user. To facilitate data clustering, it has been suggested that the user provide some supplementary… (More)

In recent years, Deep Learning has been successfully applied to multimodal learning problems, with the aim of learning useful joint representations in data fusion applications. When the available modalities consist of time series data such as video, audio and sensor signals, it becomes imperative to consider their temporal structure during the fusion… (More)

- Radha Chitta, Anil K. Jain, Rong Jin
- PIKM@CIKM
- 2015

In clustering applications involving documents and images, in addition to the large number of data points (<i>N</i>) and their high dimensionality (<i>d</i>), the number of clusters (<i>C</i>) into which the data need to be partitioned is also large. Kernel-based clustering algorithms, which have been shown to perform better than linear clustering… (More)

- Radha Chitta
- Encyclopedia of Data Warehousing and Mining
- 2009