Majority of the learning algorithms used for the training of feedforward neural networks
(FNNs), such as backpropagation (BP), conjugate gradient method, etc. rely on the …

Many existing clustering methods produce clusters with clear and sharp boundaries, which
does not truly reflect the fact that a cluster may not necessarily have a well-defined boundary …

Efficiently detecting outliers or anomalies is an important problem in many areas of science,
medicine and information technology. Applications range from data cleaning to clinical …

The traditional k-means, which unambiguously assigns an object precisely to a single
cluster with crisp boundary, does not adequately show the fact that a cluster may not have a …

The problem of efficiently finding the best match for a query in a given set with respect to the
Euclidean distance or the cosine similarity has been extensively studied. However, the …

Generalized linear models (GLMs) such as logistic regression are among the most widely
used arms in data analyst's repertoire and often used on sensitive datasets. A large body of …

Cluster ensembles generate a large number of different clustering solutions and combine
them into a more robust and accurate consensus clustering. On forming the ensembles, the …

Distance is widely used in most lazy classification systems. Rather than using distance, we
make use of the frequency of an instance's subsets of features and the frequency-change …

There are three types of relationships between an object and a cluster, namely, belong-to
definitely, uncertain and not belong-to definitely. Most of the existing clustering algorithms …

In this paper, the Dempster–Shafer (D–S) method is used as the theoretical basis for
creating data classification systems. Testing is carried out using three popular multiple …