K-nearest neighbor (KNN) and reverse k-nearest neighbor (RkNN) are two bases of many
well-established and high-performance pattern-recognition techniques, but both of them are …

Recently, there has been a significant advancement in the water quality index (WQI) models
utilizing data-driven approaches, especially those integrating machine learning and artificial …

Unsupervised k-nearest-neighbor-based outlier detectors play a vital role in data science
research. However, the detectors' performance relies on the choice of the parameter k …

Outlier detection is an important task in data mining with numerous applications, including
credit card fraud detection, video surveillance, etc. Although many Outlier detection …

Clustering analysis has been widely used in statistics, machine learning, pattern recognition,
image processing, and so on. It is a great challenge for most existing clustering algorithms to …

Watershed clustering utilizes the concept of watershed algorithm to process clustering or
cluster analyzes. The most attractive characteristic of this method is the capability to …

The problem of outlier detection is one of the most important issues in the field of analysis
due to its applicability in several famous problem domains, including intrusion detection …

Outlier detection is an important task in data mining with numerous applications, including
credit card fraud detection, video surveillance, etc. Outlier detection has been widely …

How to tackle high dimensionality of data effectively and efficiently is still a challenging issue
in machine learning. Identifying anomalous objects from given data has a broad range of …

Outliers has been studied in a variety of domains including Big Data, High dimensional data,
Uncertain data, Time Series data, Biological data, etc. In majority of the sample datasets …