Clustering and Classification Methods

Problem Solving by English Learners and English Primary Students in an Algebra Readiness ITS

by Federico Cirett Galán

Ageing Spatial Patterns: trends on Oporto's metropolitan region

by Catarina Pina

HYDROLOGIC HOMOGENEOUS REGIONS USING MONTHLY STREAMFLOW IN TURKEY

by Ercan Kahya

Co-authored with. "M.C. Demirel and A.O. Bég"
Earth Sciences Research Journal, Vol. 12, No 2, 181-193, 2008.

Cluster analysis of gauged streamflow records into homogeneous and robust regions is an important tool for the... more Cluster analysis of gauged streamflow records into homogeneous and robust regions is an important tool for the characterization of hydrologic systems. In this paper we applied the hierarchical cluster analysis to the task of objectively classifying streamflow data into regions encompassing similar streamflow patterns over Turkey. The performance of three standardization techniques was also tested, and standardizing by range was found better than standardizing with zero mean and unit variance. Clustering was carried out using Ward’s minimum variance method which became prominent in managing water resources with squared Euclidean dissimilarity measures on 80 streamflow stations. The stations have natural flow regimes where no intensive river regulation had occurred. A general conclusion drawn is that the zones having similar streamflow pattern were not be over- lapped well with the conventional climate zones of Turkey; however, they are coherent with the climate zones of Turkey recently redefined by the cluster analysis to total precipitation data as well as homogenous streamflow zones of Turkey determined by the rotated principal component analysis. The regional streamflow information in this study can significantly improve the accuracy of flow predictions in ungauged watersheds.

Key words: Cluster analysis, Ward’s method, streamflow, homogeneous region, regionalization, Turkey

A Parallel Workflow for Online Correlation and Clique-finding -- with applications to finance

by Camilo Rostoker

This thesis investigates how a state-of-the-art Stochastic Local Search (SLS) algorithm for the maximum clique problem... more This thesis investigates how a state-of-the-art Stochastic Local Search (SLS) algorithm for the maximum clique problem can be adapted for and employed within a fully distributed parallel workfiow environment. First we present parallel variants of Dynamic Local Search (DLS-MC) and Phased Local Search (PLS), demonstrating how a simple yet effective multiple independent runs strategy can offer superior speedup performance with minimal communication overhead. We then extend PLS into an online algorithm so that it can operate in a dynamic environment where the input graph is constantly changing, and show that in most cases trajectory continuation is more efficient than restarting the search from scratch. Finally, we embed our new algorithm within a data processing pipeline that performs high throughput correlation and clique-based clustering of thousands of variables from a high-frequency data stream. For communication within and between system components, we use MPI, the de-facto standard API for message passing in high-performance computing. We present algorithmic and system performance results using synthetically generated data streams, as well as a preliminary investigation into the applicability of our system for processing high-frequency, real-life intra-day stock market data in order to determine clusters of stocks exhibiting highly correlated short-term trading patterns.

Integrating human knowledge within a hybrid clustering-classification scheme for detecting patterns within large movement data sets

by Benjamin Fowler

Co-authored with René Enguehard, Orland Hoeber, Rodolphe Devillers, and Wolfgang Banzhaf
Published in AGILE 2012

The visual analysis of large movement data sets can be a challenging task. This study proposes an approach for... more The visual analysis of large movement data sets can be a challenging task. This study proposes an approach for identifying interesting movement patterns that combines human knowledge and decision making with a hybrid clustering-classification method. Rather than performing an unsupervised clustering of the entire data set, a stratified random sample of the full data set is used to identify initial clusters that are verified and labelled by the analyst, and then used as input patterns for classifying the remainder of the data set using an iterative genetic program. Classifications suggested after each iteration are presented to the analyst for refinement based on their knowledge and experience. A geovisual analytics environment is provided to both show the outcomes of the clustering and classification, and to obtain the analyst’s input during the hybrid clustering-classification process. Our approach allows data to be classified without a priori specification of classification patterns. Instead, the process takes advantage of human decision making within the automatic analysis of the data. The approach was tested with fishing vessel movement data in Eastern Canada.

Biogeography of fruit bats in Southeast Asia. Les S. Hall, Gordon G. Grigg, Craig Moritz, Besar Ketol, Isa Sait, Wahab Marni and M.T. Abdullah.

by M.T. Abdullah, PhD

Should read Abdullah (2003).

We studied on the biogeography and diversity of fruit bats in Southeast Asia, from Borneo to Asian Mainland.The... more We studied on the biogeography and diversity of fruit bats in Southeast Asia, from Borneo to Asian Mainland.The patterns of distribution of distribution, diversity and abundance are related to ecological and biogeographical factors and possible past Pleistocene events.

Les S. Hall, Gordon G. Grigg, Craig Moritz, Besar Ketol, Isa Sait, Wahab Marni and M.T. Abdullah. 2004. Sarawak Museum Journal 81: 191-284.

Self-Organising Maps: An Objective Method for Clustering Complex Human Movement

by Peter Lamb

Lamb, P., Bartlett, R., & Robins, A.
International Journal of Computer Science in Sport, Volume 9, Edition 2, Pages 20-29, 2010.

In this study self-organising maps (SOM) were used to classify the coordination patterns of four participants... more In this study self-organising maps (SOM) were used to classify the coordination patterns of four participants performing three different types of basketball shot from different distances. The shots were the free throw, the three-point and the hook shot. The free throw and three-point shot were hypothesised to be more similar to one another than to the hook shot. The first analysis involved an analysis of trial trajectories visualised on a U-matrix. Two of the participants, unexpectedly, showed more similarity between the three-point shot and the hook shot, instead of the free throw. Where the first analysis was useful in showing aspects of the movement that were not obvious from viewing the computer animation of the original movement, a second SOM was trained on the appearance of the original trajectories and used to produce an output that shows the variability in coordination between all trials in the study. The second SOM showed groupings of the three shooting conditions which were unexpected. The second SOM technique may provide a more objective method than visual technique analysis for explaining movement patterning and structuring practice routines.

Dynamic Patterns of Time Use in Adolescence

by Brian Flaherty

Automated Red Blood Cell Counting

by Alaa Hamouda

One of the vital information that help diagnosis
many of the patients’ sicknesses is the Red Blood Cell (RBC)... more One of the vital information that help diagnosis
many of the patients’ sicknesses is the Red Blood Cell (RBC)
count. In fact a normal red blood cell count helps the body to
perform nearly every function involved with surviving. At the
same time counting RBC using traditional methods is very costly
and time consuming. In addition, it is often an arduous task to get a precise and accurate result for red blood cells counting using manual cell counting. This paper introduces an efficient method for RBC and the concept of RBC.

SAARAS: Systematic Assessment And Reform Of Archaeological Systematics (Proposal)

by Kevin C. Nolan

Web Services Non-Functional Classification to Enhance Discovery Speed

by Mamoun Jamous

State space reduction in modeling traffic network dynamics for dynamic routing under ITS

by Lena Mashayekhy

Authors: M. Movahednejad, L. Mashayekhy, A. Taghavi and R. Chinnam

Published in: Proc. of the 14th International IEEE Conference on Intelligent Transportation Systems (ITSC 11), pp. 277-282, Washington DC, USA, October 2011.

State space reduction in modeling traffic network dynamics for dynamic routing under ITS

by Mahyar Movahednejad

Authors: M. Movahednejad, L. Mashayekhy, A. Taghavi and R. Chinnam

Published in: Proc. of the 14th International IEEE Conference on Intelligent Transportation Systems (ITSC 11), pp. 277-282, Washington DC, USA, October 2011.

E-Negotiation Model Based on Data Mining

by Lena Mashayekhy

Building and Construction Classification Systems

by Eric Lou

Lou, E.C.W. & Goulding, J.S. (2008), 'Building and Construction Classification Systems', Architectural Engineering and Design Management, 4(3/4), pp.206-220

Within the construction industry, there is an increasing demand for information, especially with constantly changing... more Within the construction industry, there is an increasing demand for information, especially with constantly changing products, technological developments and solutions pervading the marketplace. In this context, the idea of an `ultimate classification system' has often been seen as a misnomer, or a bridge too far. While it is acknowledged that a multiplicity of different classifications exist around the world, the incongruent and disparate nature of them could (and should) be revisited. This would help to provide clarity and unity (if nothing else) and presents a more holistic view of existing classification systems in order to provide the basic rubrics for future classification systems.

Semantic Clustering for Complex Data Items

by Nico Cinefra

Politecnico di Milano, Technical Report, February 2012

From a general point of view, semantic clustering is a software technique (a class of algorithms and methods) that... more From a general point of view, semantic clustering is a software technique (a class of algorithms and methods) that aims to group source artifacts (e.g., sentences, user profiles, items) based on their semantic value. This concerns with partitioning points of a data set into distinct groups (clusters) such that two points from one cluster are semantically similar to each other whereas two points from distinct clusters are not. In order to do this, a similarity measure is required to be passed as input to the clustering function. In this report I am trying to indagate about key features of complex data semantic classification models and their main operational aspects. The goal of these techniques, different from the classical data mining approaches, is to discover a path to a more wide and general knowledge taking advantage of the meanings related to the components and associating them with each semantic unit. Then, in the last section, I will investigate about possible applications of the techniques described above, without avoiding personal considerations.

Meta-learning: searching in the model space

by Wlodzislaw Duch

SBL-PM: A Simple Algorithm for Selection of Reference Instances in Similarity Based Methods

by Wlodzislaw Duch

Feature Space Mapping as a universal adaptive system

by Wlodzislaw Duch

Similarity-based methods: a general framework for classification, approximation and association

by Wlodzislaw Duch