Useful academic references for data mining and usage statistics

Relates to the following software for analysing Blackboard stats http://www.edshare.soton.ac.uk/11134/ Is supporting material for the following podcast: http://youtu.be/yHxCzjiYBoU

Aplicação de Data Mining à Segurança do Trabalho Ferroviário

Embora o objectivo de redução de acidentes laborais seja frequentemente invocado para justificar uma aplicação preventiva de testes de álcool e drogas no trabalho, há poucas evidências estatisticamente relevantes das pressupostas causalidade e correlação negativa entre a sujeição aos testes e os posteriores acidentes. Os dados de testes e dos acidentes ocorridos com os colaboradores de uma transportadora ferroviária portuguesa de âmbito nacional, durante anos recentes, começam agora a ser explorados, em busca de relações entre estas e outras variáveis biográficas. - Although the aim of reducing occupational accidents is frequently cited to justify preventive drug and alcohol testing at work, there is little statistically significant evidence of the assumed causality and negative correlation between exposure to testing and subsequent accidents. Data mining of tests and accidents involving employees of a Portuguese national wide railway transportation company, during recent years, is now beginning in search of relations between these and other biographical variables.

A customizable multi-agent system for distributed data mining

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances.

Elusive relationships within order fabales: phylogenetic analyses using matK and rbcL sequence data

The order Fabales, including Leguminosae, Polygalaceae, Quillajaceae and Surianaceae, represents a novel hypothesis emerging from angiosperm molecular phylogenies. Despite good support for the order, molecular studies to date have suggested contradictory, poorly supported interfamilial relationships. Our reappraisal of relationships within Fabales addresses past taxon sampling deficiencies, and employs parsimony and Bayesian approaches using sequences from the plastid regions rbcL (166 spp.) and matK (78 spp.). Five alternative hypotheses for interfamilial relationships within Fabales were recovered. The Shimodaira-Hasegawa test found the likelihood of a resolved topology significantly higher than the one calculated for a polytomy, but did not favour any of the alternative hypotheses of relationship within Fabales. In the light of the morphological evidence available and the comparative behavior of rbcL and matK, the topology recovering Polygalaceae as sister to the rest of the order Fabales with Leguminosae more closely related to Quillajaceae + Surianaceae, is considered the most likely hypothesis of interfamilial relationships of the order. Dating of selected crown clades in the Fabales phylogeny using penalized likelihood suggests rapid radiation of the Leguminosae, Polygalaceae, and (Quillajaceae + Surianaceae) crown clades.

A phylogenetic reappraisal of the Peltophorum group (Caesalpinieae: Leguminosae) based on the chloroplast trnL-F, rbcL and rps16 sequence data

The monophyly of the Peltophorum group, one of nine informal groups recognized by Polhill in the Caesalpinieae, was tested using sequence data from the trnL-F, rbcL, and rps16 regions of the chloroplast genome. Exemplars were included from all 16 genera of the Peltophorum group, and from 15 genera representing seven of the other eight informal groups in the tribe. The data were analyzed separately and in combined analyses using parsimony and Bayesian methods. The analysis method had little effect on the topology of well-supported relationships. The molecular data recovered a generally well-supported phylogeny with many intergeneric relationships resolved. Results show that the Peltophorum group as currently delimited is polyphyletic, but that eight genera plus one undescribed genus form a core Peltophorum group, which is referred to here as the Peltophorum group sensu stricto. These genera are Bussea, Conzattia, Colvillea, Delonix, Heteroflorum (inedit.), Lemuropisum, Parkinsonia, Peltophorum, and Schizolobium. The remaining eight genera of the Peltophorum group s.l. are distributed across the Caesalpinieae. Morphological support for the redelimited Peltophorum group and the other recovered clades was assessed, and no unique synapomorphy was found for the Peltophorum group s.s. A proposal for the reclassification of the Peltophorum group s.l. is presented.

Knowledge-elicitation and data-mining: fusing human and industrial plant information

Knowledge-elicitation is a common technique used to produce rules about the operation of a plant from the knowledge that is available from human expertise. Similarly, data-mining is becoming a popular technique to extract rules from the data available from the operation of a plant. In the work reported here knowledge was required to enable the supervisory control of an aluminium hot strip mill by the determination of mill set-points. A method was developed to fuse knowledge-elicitation and data-mining to incorporate the best aspects of each technique, whilst avoiding known problems. Utilisation of the knowledge was through an expert system, which determined schedules of set-points and provided information to human operators. The results show that the method proposed in this paper was effective in producing rules for the on-line control of a complex industrial process. (C) 2005 Elsevier Ltd. All rights reserved.

Fusing data-mining and knowledge elicitation for the supervisory control of an aluminium plate mill

Knowledge-elicitation is a common technique used to produce rules about the operation of a plant from the knowledge that is available from human expertise. Similarly, data-mining is becoming a popular technique to extract rules from the data available from the operation of a plant. In the work reported here knowledge was required to enable the supervisory control of an aluminium hot strip mill by the determination of mill set-points. A method was developed to fuse knowledge-elicitation and data-mining to incorporate the best aspects of each technique, whilst avoiding known problems. Utilisation of the knowledge was through an expert system, which determined schedules of set-points and provided information to human operators. The results show that the method proposed in this paper was effective in producing rules for the on-line control of a complex industrial process.

Data-mining chess databases

This is a report on the data-mining of two chess databases, the objective being to compare their sub-7-man content with perfect play as documented in Nalimov endgame tables. Van der Heijden’s ENDGAME STUDY DATABASE IV is a definitive collection of 76,132 studies in which White should have an essentially unique route to the stipulated goal. Chessbase’s BIG DATABASE 2010 holds some 4.5 million games. Insight gained into both database content and data-mining has led to some delightful surprises and created a further agenda.

Phylogenetic systematics of Sternbergia (Amaryllidaceae) based on plastid and ITS sequence data

The phylogenetics of Sternbergia (Amaryllidaceae) were studied using DNA sequences of the plastid ndhF and matK genes and nuclear internal transcribed spacer (ITS) ribosomal region for 38, 37 and 32 ingroup and outgroup accessions, respectively. All members of Sternbergia were represented by at least one accession, except S. minoica and S. schubertii, with additional taxa from Narcissus and Pancratium serving as principal outgroups. Sternbergia was resolved and supported as sister to Narcissus and composed of two primary subclades: S. colchiciflora sister to S. vernalis, S. candida and S. clusiana, with this clade in turn sister to S. lutea and its allies in both Bayesian and bootstrap analyses. A clear relationship between the two vernal flowering members of the genus was recovered, supporting the hypothesis of a single origin of vernal flowering in Sternbergia. However, in the S. lutea complex, the DNA markers examined did not offer sufficient resolving power to separate taxa, providing some support for the idea that S. sicula and S. greuteriana are conspecific with S. lutea