s of the Keynotes Health Data Management and Analytics with Privacy and Confidentiality
Wind turbines are typically organised as a fleet in a wind park, subject to similar, but varying, environmental conditions. This makes it possible to assess and benchmark a turbine’s output performanc...
We present PSEUDo, an adaptive feature learning technique for exploring visual patterns in multi-track sequential data. Our approach is designed with the primary focus to overcome the uneconomic retra...
Time series visualization is one of the most fundamental tasks, which is often used to discover patterns by a user interface. Many increasing interests in time series visualization in the last decade ...
Time series is a sequential collection of values with respect to time obtained from various applications. The time series data have basic features like huge data size, high dimensionality with charact...
Time series discretization is a technique commonly used to tackle time series classification problems. This manuscript presents an enhanced multi-objective approach for the symbolic discretization of ...
Time series data are generated in abundance of information technology applications. Classification of time series is one of the significant areas of interest in Time Series Data Mining. Over the last ...
Time series classification is an important problem in data mining with several applications in different domains. Because time series data are usually high dimensional, dimensionality reduction techni...
Time series analysis is widely used in the fields of finance, medical, and climate monitoring. However, the high dimension characteristic of time series brings a lot of inconvenience to its applicatio...
Throughout recent years, dynamic time warping (DTW) has remained as a robust similarity measure in time series classification (TSC). 1-nearest neighbor (1-NN) algorithm with DTW is the most widely use...
This thesis concerns the field of computational intelligence (CI), an important area of computer science that predominantly endeavours to model complex systems with heuristic algorithms. Heuristic alg...
This chapter introduces a method that discovers characteristic sequential patterns from sequential data based on background knowledge. The sequential data is composed of rows of items. This chapter fo...
There are many factors that can contribute to corrosion in the pipeline. Therefore, it is important for decision makers to analyze and identify the main factor of corrosion in order to take appropriat...
The recording and analyzing human motor control movements are fundamental parts of both behavioral biometrics and biomedical research studies. The dynamics of human motor functions of fingers, hand an...
The objective of this thesis is considering the safety analysis of the simplified cooling system of a single module of the ITER Central Solenoid in a cold test facility to classify the different abnor...
The massive collection of data via emerging technologies like the Internet of Things (IoT) requires finding optimal ways to reduce the observations in the time series analysis domain. The IoT time ser...
The ever-increasing volume and complexity of time series data, emerging in various application domains, necessitate efficient dimensionality reduction for facilitating data mining tasks. Symbolic repr...
The detection of outliers in time series data is a core component of many data-mining applications and broadly applied in industrial applications. In large data sets algorithms that are efficient in b...
The abundance and value of mining large time series data sets has long been acknowledged. Ubiquitous in fields ranging from astronomy, biology and web science the size and number of these datasets con...
The Piecewise Aggregate Approximation (PAA) is widely used in time series data mining because it allows to discretize, to reduce the length of time series and it is used as a subroutine by algorithms ...