While Anomaly Detection is commonly accepted as an appropriate technique to uncover yet unknown network misuse patterns and malware, detection rates are often diminished by, e.g., unpredictable use...
We address the problem of visualizing and interacting with large multi-dimensional time- series data. We propose a visual analytics system and approach which aims to visualize, analyze, present and en...
Today, real world time series data sets can take a size up to a trillion observations and even more. Data miners’ task is it to detect new information that is hidden in this massive amount of data. Wh...
Time series data is pervasive in many applications and the anomaly detection about it is important, which will provide the early warning of some unexpected patterns. In this paper, we propose a multip...
This publication presents a survey on the clustering algorithms proposed for spatiotemporal data. We begin our study with definitions of spatiotemporal datatypes. Next we provide a categorization of s...
This paper proposes the use of mixed fuzzy clustering (MFC) algorithm to derive Takagi–Sugeno (T–S) fuzzy models (FMs). Mixed fuzzy clustering handles both time invariant and multivariate time variant...
This paper presents two mathematical models representing imprecise capacitated fixed-charge transportation problems for a two-stage supply chain network in Gaussian fuzzy type-2 environment. It is a t...
The process of mining includes various methodologies and data classification is one of the advantageous methods involved in it. It not only eases the process of machine learning but also gives a platf...
The process of mining includes data classification which is one of the most beneficial and constructive methods. As the data is missed during classification process, it if affected on a very large sca...
The process of mining comprises of supervised learning and unsupervised learning. It includes various approaches out of which data classification is one of the beneficial and constructive methods. Thi...
The problem of anomaly detection in time series has received a lot of attention in the past two decades. However, existing techniques cannot locate where the anomalies are within anomalous time series...
The problem of anomaly detection in time series has received a lot of attention in the past two decades. However, existing techniques cannot locate where the anomalies are within anomalous time series...
The probability density function represents the uncertainty of time series at each time point. In this paper, based on probability density function, we adopt the ULDTW distance for uncertain time seri...
The one-class classification problem is a well-known research endeavor in pattern recognition. The problem is also known under different names, such as outlier and novelty/anomaly detection. The core ...
The application of anomaly detection to data monitoring is a fundamental requirement of the public service systems of a smart city. Many detection methods have been proposed for identifying anomalous ...
Spatiotemporal streams are prone to data quality issues such as missing, duplicated and delayed data—when data generating sensors malfunction, data transmissions experience problems, or when data are ...
Spatial time series is a common type of data dealt with in many domains, such as economic statistics and environmental science. There have been many studies focusing on finding and analyzing various k...
Recent clustering based anomaly detection technologies classify new observations in different ways, e.g. using probability distributions, cluster centers or whole data points. Some of which suffer fro...
Real time data analysis in data streams is a highly challenging area in big data. The surge in big data techniques has recently attracted considerable interest to the detection of significant changes ...
Performance monitoring, anomaly detection, and root-cause analysis in complex cyber-physical systems (CPSs) are often highly intractable due to widely diverse operational modes, disparate data types, ...