The Ensemble of Unsupervised Incremental Learning Algorithm For Time Series Data

Data mining is one of the key concepts to discover hidden knowledge from available data. along with the data mining, data analytics is a field to analyze and process data in a scientific and cognitive angle. it is more helpful to convert knowledge to actionable knowledge for accurate decision making. data stream mining is another challenging area than normal data mining due to its dynamics. dynamics of data in a stream includes changes in data frequency, volume and nature. this pap er focuses on the behavior of data mining of machines in process/manufacturing industries. in general, such data is continuous numerical and time series data captured by various industrial sensors. by nature, equipment or machinery behaviour can change over time. it requires calibration/replacement before failure of machinery. by analyzing data, one can find the behavior or state change. to identify that, dynamic models are required to be built using data mining and data stream mining. thus, we are following a seminovel approach for building such models using “ensemble of unsupervised incremental learning method. results show how the existing methods are different from the proposed method. this method can be applied for any other domain like image/audio/video or text mining.