Abstract: Customer churn prediction is one of the most useful
areas of study in customer analytics. Due to the enormous amount
of data available for such predictions, machine learning and data
mining have been heavily used in this domain. There exist many
machine learning algorithms directly applicable for the problem of
customer churn prediction, and here, we attempt to experiment on
a novel approach by using a cognitive learning based technique in
an attempt to improve the results obtained by using a combination
of supervised learning methods, with cognitive unsupervised learning
methods.
Abstract: Textual data plays an important role in the modern
world. The possibilities of applying data mining techniques to
uncover hidden information present in large volumes of text
collections is immense. The Growing Self Organizing Map (GSOM)
is a highly successful member of the Self Organising Map family
and has been used as a clustering and visualisation tool across wide
range of disciplines to discover hidden patterns present in the data.
A comprehensive analysis of the GSOM’s capabilities as a text
clustering and visualisation tool has so far not been published. These
functionalities, namely map visualisation capabilities, automatic
cluster identification and hierarchical clustering capabilities are
presented in this paper and are further demonstrated with experiments
on a benchmark text corpus.