Unsupervised Event Detection Research Articles

Unsupervised Event Detection, Clustering, and Use Case Exposition in Micro-PMU Measurements

Distribution-level phasor measurement units, a.k.a, micro-PMUs, report a large volume of high resolution phasor measurements which constitute a variety of event signatures of different phenomena that occur all across power distribution feeders. In order to implement an event-based analysis that has useful applications for the utility operator, one needs to extract these events from a large volume of micro-PMU data. However, due to the infrequent, unscheduled, and unknown nature of the events, it is often a challenge to even figure out what kind of events are out there to capture and scrutinize. In this paper, we seek to address this open problem by developing an unsupervised approach, which requires minimal prior human knowledge. First, we develop an unsupervised event detection method based on the concept of Generative Adversarial Networks (GAN). It works by training deep neural networks that learn the characteristics of the normal trends in micro-PMU measurements; and accordingly detect an event when there is any abnormality. We also propose a two-step unsupervised clustering method, based on a novel linear mixed integer programming formulation. It helps us categorize events based on their origin in the first step and their similarity in the second step. The active nature of the proposed clustering method makes it capable of identifying new clusters of events on an ongoing basis. The proposed unsupervised event detection and clustering methods are applied to real-world micro-PMU data. Results show that they can outperform the prevalent methods in the literature. These methods also facilitate our further analysis to identify important clusters of events that lead to unmasking several use cases that could be of value to the utility operator.

Unsupervised Event Detection Using Self-learning-based Max-margin Clustering: Analysis on Streaming Tweets

ABSTRACT We propose an unsupervised approach for tweet clustering from large-scale Twitter repository in this paper. The amount of acquired data from streaming media like Twitter is vast in nature. They contain readily available information regarding important events taking place during the time span. Hence, it is indeed difficult to deploy supervised learning strategies for analyzing the tweets for meaningful information extraction. On top of that, the tweets are unstructured in nature given the diversities of the end-users who put the tweets. Given that, an unsupervised tweet-processing technique can be of immense help for different inference tasks including event extraction, sentiment analysis, to name a few. Based on the aforementioned bottlenecks of the majority of the existing techniques, we propose a novel unsupervised event detection strategy from streaming tweets. In this regard, we propose a self-learning max-margin clustering which deploys the notion of SVM in an unsupervised setup. We evaluate proposed system and compare it with the popular techniques from the literature using 6.5 million streaming tweets, collected in June 2017. In our experiments, self-learning-based max-margin clustering outperforms the techniques of literature in terms of precision, Silhouette score, and Calinski–Harabasz score.

Unsupervised event detection and classification of multichannel signals

In this paper, we present a new unsupervised method to classify a set of Multichanel Signals (MC) with unknown events. Each signal is characterized by a sequence of events where the number of events, the start time and the duration between events can change randomly. The proposed method helps in the classification and event detection of the MC signals by an expert which usually becomes a tedious and difficult task. To this end, first, the problem of classification of MC signals characterized by a succession of events is analyzed by transforming the MC signals into a set of temporal sequences of easy interpretation. The algorithm detects events by means of an optimal unsupervised classification. It is not necessary to know the nature of the events and formulate hypotheses regarding their behavior. Then, a set of multichannel electromyographic (EMG) signals with events is generated. These MC signals are used to test the proposed method.

Temporal video segmentation by event detection: A novelty detection approach

Temporal segmentation of videos into meaningful image sequences containing some particular activities is an interesting problem in computer vision. We present a novel algorithm to achieve this semantic video segmentation. The segmentation task is accomplished through event detection in a frame-by-frame processing setup. We propose using one-class classification (OCC) techniques to detect events that indicate a new segment, since they have been proved to be successful in object classification and they allow for unsupervised event detection in a natural way. Various OCC schemes have been tested and compared, and additionally, an approach based on the temporal self-similarity maps (TSSMs) is also presented. The testing was done on a challenging publicly available thermal video dataset. The results are promising and show the suitability of our approaches for the task of temporal video segmentation.