Shyam Varan Nath

Book on the Industrial Internet Architecture, co-authored with Robert Stackowiak (Microsoft) and Carla Romano (Oracle).

The selection of appropriate data mining predictive models is a challenging task. While it is easy to evaluate the model based on the historical data at a given point in time, using confusion matrix and misclassification rate, it is not very easy to ensure that the selected model upon deployment stays the most effective one as newer data comes in. Here we will address the issue of how to continually strive for the best model even after a predictive model is deployed for production use. In the… Show more

The selection of appropriate data mining predictive models is a challenging task. While it is easy to evaluate the model based on the historical data at a given point in time, using confusion matrix and misclassification rate, it is not very easy to ensure that the selected model upon deployment stays the most effective one as newer data comes in. Here we will address the issue of how to continually strive for the best model even after a predictive model is deployed for production use. In the champion-challenger based model selection paradigm, the historical data is used for creating the best or the champion predictive model using criteria like misclassification rate for a given cost matrix. Apart from the champion models, a number of other models are selected which are not as good as the champion model in predictive accuracy using same data. These models are termed as challengers to the current champion model. These models may differ from the champion model in the underlying predictive algorithm, algorithm tuning parameters or in use of model attributes. The predictive modeling starts with the conventional processes such as identifying the business problem that warrants the need for predictive modeling, finding the significant attributes for modeling, data quality analysis, followed by the actual modeling building and evaluation of the models. However, the emphasis is not at finding just the top or the champion model but to find the other models that are close in terms of model performance. The guiding principle here is that the selection of the best predictive model based on the current set of historical data, is not the stamp of approval till eternity. Real world systems that use predictive modeling are complex and dynamic processes and need to incorporate means to capture that. When the champion model is deployed in a production system and is used for predictions, these results are saved in a table. Likewise the challenger models are also used to score a subset. Show less

Solving crimes is a complex task and requires a lot of experience. Data mining can be used to model crime detection problems. The idea here is to try to capture years of human experience into computer models via data mining. Crimes are a social nuisance and cost our society dearly in several ways. Any research that can help in solving crimes faster will pay for itself. According to Los Angeles Police Department, about 10% of the criminals commit about 50% of the crimes. Here we look at use of… Show more

Solving crimes is a complex task and requires a lot of experience. Data mining can be used to model crime detection problems. The idea here is to try to capture years of human experience into computer models via data mining. Crimes are a social nuisance and cost our society dearly in several ways. Any research that can help in solving crimes faster will pay for itself. According to Los Angeles Police Department, about 10% of the criminals commit about 50% of the crimes. Here we look at use of clustering algorithm for a data mining approach to help detect the crimes patterns and speed up the process of solving crime. We will look at k-means clustering with some enhancements to aid in the process of identification of crime patterns. We applied these techniques to real crime data from a sheriff’s office and validated our results. We also used semi-supervised learning technique here for knowledge discovery from the crime records and to help increase the predictive accuracy. Our major contribution is the development of a weighting scheme for attributes, to deal with limitations of various out of the box clustering tools and techniques. This easy to implement data mining framework works with the geo-spatial plot of crime and helps to improve the productivity of the detectives and other law enforcement officers. It can also be applied for counter terrorism for homeland security. Show less

Use of Oracle Data Mining (ODM) to detect crime patterns and laying them on the Map to assist in crime detection.

The increasing reliance upon wireless networks has put tremendous' emphasis on wireless network security. While considerable attention has been given to data mining for intrusion detection in wired networks, limited focus has been devoted to data mining for intrusion detection in wireless networks. This study presents a clustering approach with tracers and expert analysis for intrusion detection in a real-...
Conference: International Conference on Machine Learning and Applications - ICMLA ,… Show more

The increasing reliance upon wireless networks has put tremendous' emphasis on wireless network security. While considerable attention has been given to data mining for intrusion detection in wired networks, limited focus has been devoted to data mining for intrusion detection in wireless networks. This study presents a clustering approach with tracers and expert analysis for intrusion detection in a real-...
Conference: International Conference on Machine Learning and Applications - ICMLA , 2005 Show less

Intrusion detection in wireless networks has become an indispensable component of any useful wireless network security systems, and has recently gained attention in both research and industry communities due to widespread use of wireless local area networks (WLANs). This paper focuses on detecting intrusions or anomalous behaviors in WLANs with data clustering techniques. We first explore the security vulnerabilities of ...
Conference: International Conference on Tools with Artificial… Show more

Intrusion detection in wireless networks has become an indispensable component of any useful wireless network security systems, and has recently gained attention in both research and industry communities due to widespread use of wireless local area networks (WLANs). This paper focuses on detecting intrusions or anomalous behaviors in WLANs with data clustering techniques. We first explore the security vulnerabilities of ...
Conference: International Conference on Tools with Artificial Intelligence - ICTAI
Shi Zhong, Taghi M. Khoshgoftaar, Shyam Varan Nath Show less