In this paper we focus on the anomaly detection problem for dynamic data streams through the lens of random cut forests. We investigate a robust random cut data structure that can be used as a sketch or synopsis of the input stream. We provide a plausible definition of non-parametric anomalies based on the influence of an unseen point on the remainder of the data, i.e., the externality imposed ...

We discuss a temporal text mining task on finding evolutionary patterns of topics from a collection of article revisions. To reveal the evolution of topics, we propose a novel method for finding key phrases that are bursty and significant in terms of revision histories. Then we show a time series clustering method to group phrases that have similar burst histories, where additions and deletions...

Cluster and outlier detection has always been one of data mining research interests. Numerous approaches have been designed to find clusters and detect outliers in various types of data sets. In this paper, we present our research on analyzing data sets with constant changes. We design approaches to keep track of status of clusters, the movement of data points, and the updated group of outliers...

The Outlier detection is currently area of active research in data set mining community. In this article we propose hybrid approach to capture outliers in dynamic data stream. We apply k-mean algorithm which Partition the data set into number of chunks or clusters. Each chunk contains set of data. Once cluster are formed, centroid of each cluster are calculated. The points which are lying near ...

Outlier detection has significant importance in the data mining domain. Applications which contain streaming data flow may have many abnormal or outlier data and these applications require efficient outlier detection techniques to detect and analyze these abnormal patterns. Outlier detection is the process of detecting patterns in the data which do not adhere to the normal behavior or data. The...

ABSTRACT Dynamic networks, also called network streams, are an important data representation that applies to many real-world domains. Many sets of network data such as e-mail networks, social networks, or internet traffic networks are best represented by a dynamic network due to the temporal component of the data. One important application in the domain of dynamic network analysis is anomaly de...

The emerging field of Ecosystem Informatics applies methods from computer science and mathematics to address fundamental and applied problems in the ecosystem sciences. The ecosystem sciences are in the midst of a revolution driven by a combination of emerging technologies for improved sensing and the critical need for better science to help manage global climate change. This paper describes se...

Wireless Sensor Networks (WSNs) have emerged as one of the most important research areas, large numbers of limited resource sensor nodes are used to monitor the physical environment and report any significant information. Many different anomaly detection systems (ADS) have been proposed in the literature over the years. Now apply an algorithm to increase detection sensitivity. Detection of sens...