Nurjahan Begum

• University of California, Riverside

The last decade has seen a flurry of research on all-pairs-similarity-search (or similarity joins) for text, DNA and a handful of other datatypes, and these systems have been applied to many diverse data mining problems. However, there has been surprisingly little progress made on similarity joins for time series subsequences. The lack of progress...

Time Series Clustering is an important subroutine in many higher-level data mining analyses, including data editing for classifiers, summarization, and outlier detection. It is well known that for similarity search the superiority of Dynamic Time Warping (DTW) over Euclidean distance gradually diminishes as we consider ever larger datasets. However...

The research community seems to have converged in agreement that for time series classification problems, Dynamic Time Warping (DTW)-based nearest-neighbor classifiers are exceptionally hard to beat. Obtaining the best performance from DTW requires setting its only parameter, the warping window width (w). This is typically set by cross validation i...

Clustering time series is a useful operation in its own right, and an important subroutine in many higher-level data mining analyses, including data editing for classifiers, summarization, and outlier detection. While it has been noted that the general superiority of Dynamic Time Warping (DTW) over Euclidean Distance for similarity search diminishe...

A recently introduced primitive for time series data mining, unsupervised shapelets (u-shapelets), has demonstrated significant potential for time series clustering. In contrast to approaches that consider the entire time series to compute pairwise similarities, the u-shapelets technique allows considering only relevant subsequences of time series....

The detection of time series motifs, which are approximately repeated subsequences in time series streams, has been shown to have great utility as a subroutine in many higher-level data mining algorithms. However, this detection becomes much harder in cases where the motifs of interest are vanishingly rare or when faced with a never-ending stream o...

In recent years the plunging costs of sensors/storage have made it possible to obtain vast amounts of medical telemetry, both in clinical settings and more recently, even in patient’s own homes . However for this data to be useful, it must be annotated. This annotation, requiring the attention of medical experts is very expensive and time consuming...

In the last decade the plunging costs of sensors/storage have made it possible to obtain vast amounts of medical telemetry. However for this data to be useful, it must be annotated. This annotation, requiring the attention of medical experts is very expensive and time consuming, and remains the critical bottleneck in medical analysis. Semi-supervis...

In a wireless sensor network, it is a common practice to have in network partial data processing. As part of this, the mitigation of the demands issued by sink nodes in a heterogeneous resource constrained sensor network requires optimal resource sharing, which is a very challenging task. We find this task analogous to finding the optimal flow in a...