EXPLORE PUBLICATIONS BY COUNTRIES

Data Stream, Concept Change, Precision, Recall, F1 Measure, Cumulative Density Function

In the recent years, data streams have been in the gravity of focus of quite a lot number of researchers in different domains. All these researchers share the same difficulty when discovering unknown pattern within data streams that is concept change. The notion of concept change refers to the places where underlying distribution of data changes from time to time. There have been proposed different methods to detect changes in the data stream but most of them are based on an unrealistic assumption of having data labels available to the learning algorithms. Nonetheless, in the real world problems labels of streaming data are rarely available. This is the main reason why data stream communities have recently focused on unsupervised domain. This study is based on the observation that unsupervised approaches for learning data stream are not yet matured; namely, they merely provide mediocre performance specially when applied on multi-dimensional data streams. In this paper, we propose a method for Tracking Changes in the behavior of instances using Cumulative Density Function; abbreviated as TrackChCDF. Our method is able to detect change points along unlabeled data stream accurately and also is able to determine the trend of data called closing or opening. The advantages of our approach are three folds. First, it is able to detect change points accurately. Second, it works well in multi-dimensional data stream, and the last but not the least, it can determine the type of change, namely closing or opening of instances over the time which has vast applications in different fields such as economy, stock market, and medical diagnosis. We compare our algorithm to the state-of-the-art method for concept change detection in data streams and the obtained results are very promising.