Document clustering can be applied in document organisation and browsing, document
summarisation and classification. The identification of an appropriate representation for …

There has been a meteoric rise in the total amount of digital texts as a direct result of the
proliferation of internet access. As a direct result of this, document clustering has evolved …

In order to make better business decisions, faster database browsing and reducing
processing time of queries, Extraction of Information from text documents in efficient manner …

The document data is growing at an exponential rate. It is heterogeneous, dynamic and
highly unstructured in nature. These characteristics of document data pose new challenges …

Scalable big data analysis frameworks are of paramount importance in the modern web
society, which is characterized by a huge number of resources, including electronic text …

Document clustering, one of the traditional data mining techniques, is an unsupervised
learning paradigm where clustering methods try to identify inherent groupings of the text …

With the rapid growth of knowledge, it shows a steady trend of knowledge fragmentization.
Knowledge fragmentization manifests as that the knowledge related to a specific topic in a …

Document clustering is an unsupervised approach in which a large collection of documents
(corpus) is subdivided into smaller, meaningful, identifiable, and verifiable sub-groups …

Search queries define a set of documents located in a collection and can be used to rank the
documents by assigning each document a score according to their closeness to the query in …

The objective of this study was to maximize the conversion of saponification reaction in a
continuous stirred tank reactor (CSTR). Full two-level factorial design and response surface …