Varun Chandola - Academia.edu (original) (raw)

Papers by Varun Chandola

ACM Computing Surveys, 2009

Anomaly detection is an important problem that has been researched within diverse research areas ... more Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and m...

Sigkdd Explorations, 2010

Wide-area sensor infrastructures, remote sensors, RFIDs, and wireless sensor networks yield massi... more Wide-area sensor infrastructures, remote sensors, RFIDs, and wireless sensor networks yield massive volumes of disparate, dynamic, and geographically distributed data. As such sensors are becoming ubiquitous, a set of broad requirements is beginning to emerge across high-priority applications including adaptability to climate change, electric grid monitoring, disaster preparedness and management, national or homeland security, and the management of critical infrastructures.

SIGKDD explorations, 2010

The empirical study of machine learning and data mining methods often falls prey to the effects o... more The empirical study of machine learning and data mining methods often falls prey to the effects of publication bias that favors positive results over negative ones. Most, if not all, articles in conferences and journals report only positive results. This does not reflect the practice of a field where failures happen regularly. As in real life, we often learn more from negative results than we do from positive ones. It is time that we, as a community, start to regard failures as being as informative as successes. After all, we do know the difficulty of ...

ACM Computing Surveys, 2009

Anomaly detection is an important problem that has been researched within diverse research areas ... more Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and m...

Sigkdd Explorations, 2010

Wide-area sensor infrastructures, remote sensors, RFIDs, and wireless sensor networks yield massi... more Wide-area sensor infrastructures, remote sensors, RFIDs, and wireless sensor networks yield massive volumes of disparate, dynamic, and geographically distributed data. As such sensors are becoming ubiquitous, a set of broad requirements is beginning to emerge across high-priority applications including adaptability to climate change, electric grid monitoring, disaster preparedness and management, national or homeland security, and the management of critical infrastructures.

SIGKDD explorations, 2010

The empirical study of machine learning and data mining methods often falls prey to the effects o... more The empirical study of machine learning and data mining methods often falls prey to the effects of publication bias that favors positive results over negative ones. Most, if not all, articles in conferences and journals report only positive results. This does not reflect the practice of a field where failures happen regularly. As in real life, we often learn more from negative results than we do from positive ones. It is time that we, as a community, start to regard failures as being as informative as successes. After all, we do know the difficulty of ...