Studies the problem of maintaining materialized views of graph structured data. The base
data consists of records containing identifiers of other records. The data could represent …

The aim of collaborative clustering is to reveal the common structure of data distributed on
different sites. In this paper, we present a formalism of topological collaborative clustering …

The aim of collaborative clustering is to reveal the common structure of data which are
distributed on different sites. The topological collaborative clustering (based on Kohonen …

Decision on reservoir water release is crucial during both intense and less intense rainfall
seasons. Even though reservoir water release is guided by the procedures, decision usually …

During emergencies such as flood and drought seasons, reservoir acts as a defence
mechanism to reduce the risk of flooding and maintaining water supply. During this period …

We propose in this paper a novel approach which makes self-organizing maps (SOM) and
the Hidden Markov Models (HMMs) cooperate. Our approach (SOS-HMM: Self Organizing …

Graph clustering techniques are very useful for detecting densely connected groups in large
graphs. Many existing graph clustering methods mainly focus on the topological structure …

The vertical collaborative clustering aims to unravel the hidden structure of data (similarity)
among different sites, which will help data owners to make a smart decision without sharing …

In this paper we present Casep2: a hybrid neuro-symbolic system combining case-based
reasoning (CBR) and artificial neural networks that aims at clustering and classifying users' …

In this study, we introduce a novel hybrid collaboration clustering architecture, in which
several subsets of patterns can be processed together with an objective of finding a common …