Existing fault detection and diagnosis methods for chillers are usually very effective on
single faults diagnosis, but perform poorly while diagnosing multiple faults, and these fault …

One of the widely used classification algorithms is k-Nearest Neighbours (k-NN). Its
popularity is mainly due to its simplicity, effectiveness, ease of implementation and ability to …

Clustering evaluation plays an important role in unsupervised learning systems, as it is often
necessary to automatically quantify the quality of generated cluster configurations. This is …

Most machine learning tasks involve learning from high-dimensional data, which is often
quite difficult to handle. Hubness is an aspect of the curse of dimensionality that was shown …

Instance reduction for K-nearest-neighbor classification rules (KNN) has attracted much
attention these years, and most of the existing approaches lose the semantics of probability …

Time-series classification is the common denominator in many real-world pattern recognition
tasks. In the last decade, the simple nearest neighbor classifier, in combination with dynamic …

The k nearest neighbor (k NN) rule is one of the most used techniques in data mining and
pattern recognition due to its simplicity and low identification error. However, the …

Learning with label noise is an important issue in classification, since it is not always
possible to obtain reliable data labels. In this paper we explore and evaluate a new …

Traditional nearest neighbor classifiers based on usual distance functions (eg, Euclidean
distance) often suffer in high dimension low sample size (HDLSS) situations, where …

Extracting valuable insights from a large volume of unstructured data such as texts through
clustering analysis is paramount to many big data applications. However, document …