An incremental and growing network model is introduced which is able to learn the
topological relations in a given set of input vectors by means of a simple Hebb-like learning …

Histogram pyramid representations computed from a vocabulary tree of visual words have
proven valuable for a range of image indexing and recognition tasks; however, they have …

One of the main challenges in the design of modern clustering algorithms is that, in many
applications, new data sets are continuously added into an already huge database. As a …

As the sizes of many contemporary databases continue to grow rapidly, incremental
clustering has emerged as an essential issue for conducting data analysis on contemporary …

In this paper, we present a new heuristic measure for optimizing database used as input
layer of Self Organizing Map (SOM). This heuristic called Hl-SOM (Heuristic Input for SOM) …

In recent years, the expansion of acquisition devices such as digital cameras, the
development of storage and transmission techniques of multimedia documents and the …

Hierarchical clustering of text collections is a key problem in document management and
retrieval. In partitional hierarchical clustering, which is more efficient than its agglomerative …

An incremental and Growing network model is introduced which is able to learn the
topological relations in a given set of input vectors by means of a simple Hebb-like learning …

SAHN is a widely used agglomerative hierarchical clustering method. Nevertheless it is not
an incremental algorithm and therefore it is not suitable for many real application areas …

This paper proposes a new text clustering algorithm based on a tree structure. The main
idea of the clustering algorithm is a sub-tree at a specific node represents a document …