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 …

Clustering has primarily been used as an analytical technique to group unlabeled data for
extracting meaningful information. The fact that no clustering algorithm can solve all …

Subspace clustering is an effective method that has been successfully applied to many
applications. Here, we propose a novel subspace clustering model for multi-view data using …

Recent advances in high-throughput technologies have enabled the profiling of multiple
layers of a biological system, including DNA sequence data (genomics), RNA expression …

Clustering is an essential tool in data mining research and applications. It is the subject of
active research in many fields of study, such as computer science, data science, statistics …

Offspring generation plays an important role in evolutionary multiobjective optimization.
However, generating promising candidate solutions effectively in high-dimensional spaces …

Accurate land cover information is required for science, monitoring, and reporting. Land
cover changes naturally over time, as well as a result of anthropogenic activities. Monitoring …

This is an overview of the end-to-end data cleaning process. Data quality is one of the most
important problems in data management, since dirty data often leads to inaccurate data …

Data analysis is used as a common method in modern science research, which is across
communication science, computer science and biology science. Clustering, as the basic …

Many real-world systems can be studied in terms of pattern recognition tasks, so that proper
use (and understanding) of machine learning methods in practical applications becomes …