Anomaly detection in high dimensional data is becoming a fundamental research problem
that has various applications in the real world. However, many existing anomaly detection …

k nearest neighbours (kNN) queries are fundamental in many applications, ranging from
data mining, recommendation system and Internet of Things, to Industry 4.0 framework …

The classification problem is closely related to the clustering problem discussed in Chaps. 6
and 7. While the clustering problem is that of determining similar groups of data points, the …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

Clustering is a widely studied data mining problem in the text domains. The problem finds
numerous applications in customer segmentation, classification, collaborative filtering …

As a prolific research area in data mining, subspace clustering and related problems
induced a vast quantity of proposed solutions. However, many publications compare a new …

Our world and our lives are changing in many ways. Communication, networking, and
computing technologies are among the most influential enablers that shape our lives today …

We investigate techniques for analysis and retrieval of object trajectories in two or three
dimensional space. Such data usually contain a large amount of noise, that has made …

The outlier detection problem has important applications in the field of fraud detection,
network robustness analysis, and intrusion detection. Most such applications are high …

Similarity search in large time series databases has attracted much research interest
recently. It is a difficult problem because of the typically high dimensionality of the data.. The …