Growing Self Organizing Map with an Imposed Binary Search Tree for Discovering Temporal Input Patterns

In this paper the Binary Search Tree Imposed Growing Self Organizing Map (BSTGSOM) is presented as an extended version of the Growing Self Organizing Map (GSOM), which has proven advantages in knowledge discovery applications. A Binary Search Tree imposed on the GSOM is mainly used to investigate the dynamic perspectives of the GSOM based on the inputs and these generated temporal patterns are stored to further analyze the behavior of the GSOM based on the input sequence. Also, the performance advantages are discussed and compared with that of the original GSOM.

Genome sequence of Ensifer arboris strain LMG 14919T, a microsymbiont of the legume Prosopis chilensis growing in Kosti, Sudan

Ensifer arboris LMG 14919T is an aerobic, motile, Gram-negative, non-spore-forming rod that can exist as a soil saprophyte or as a legume microsymbiont of several species of legume trees. LMG 14919T was isolated in 1987 from a nodule recovered from the roots of the tree Prosopis chilensis growing in Kosti, Sudan. LMG 14919T is highly effective at fixing nitrogen with P. chilensis (Chilean mesquite) and Acacia senegal (gum Arabic tree or gum acacia). LMG 14919T does not nodulate the tree Leucena leucocephala, nor the herbaceous species Macroptilium atropurpureum, Trifolium pratense, Medicago sativa, Lotus corniculatus and Galega orientalis. Here we describe the features of E. arboris LMG 14919T, together with genome sequence information and its annotation. The 6,850,303 bp high-quality-draft genome is arranged into 7 scaffolds of 12 contigs containing 6,461 protein-coding genes and 84 RNA-only encoding genes, and is one of 100 rhizobial genomes sequenced as part of the DOE Joint Genome Institute 2010 Genomic Encyclopedia for Bacteria and Archaea-Root Nodule Bacteria (GEBA-RNB) project.

Growing self-organizing map for online continuous clustering

The internet age has fuelled an enormous explosion in the amount of information generated by humanity. Much of this information is transient in nature, created to be immediately consumed and built upon (or discarded). The field of data mining is surprisingly scant with algorithms that are geared towards the unsupervised knowledge extraction of such dynamic data streams. This chapter describes a new neural network algorithm inspired by self-organising maps. The new algorithm is a hybrid algorithm from the growing self-organising map (GSOM) and the cellular probabilistic self-organising map (CPSOM). The result is an algorithm which generates a dynamically growing feature map for the purpose of clustering dynamic data streams and tracking clusters as they evolve in the data stream.

Cluster identification and separation in the growing self-organizing map: application in protein sequence classification

Growing self-organizing map (GSOM) has been introduced as an improvement to the self-organizing map (SOM) algorithm in clustering and knowledge discovery. Unlike the traditional SOM, GSOM has a dynamic structure which allows nodes to grow reflecting the knowledge discovered from the input data as learning progresses. The spread factor parameter (SF) in GSOM can be utilized to control the spread of the map, thus giving an analyst a flexibility to examine the clusters at different granularities. Although GSOM has been applied in various areas and has been proven effective in knowledge discovery tasks, no comprehensive study has been done on the effect of the spread factor parameter value to the cluster formation and separation. Therefore, the aim of this paper is to investigate the effect of the spread factor value towards cluster separation in the GSOM. We used simple k-means algorithm as a method to identify clusters in the GSOM. By using Davies–Bouldin index, clusters formed by different values of spread factor are obtained and the resulting clusters are analyzed. In this work, we show that clusters can be more separated when the spread factor value is increased. Hierarchical clusters can then be constructed by mapping the GSOM clusters at different spread factor values.

Benchmarks of Victorian commercial goat meat enterprises

The Australian goat meat industry has mostly processed feral goats for export. As goat meat markets mature there will be an increased demand for farmed goats to meet supply, especially into niche markets. Production benchmarking showed that Victorian commercial goat meat producers are located generally in areas with <500 mm rainfall, usually in conjunction with other livestock and cropping enterprises. On average, 67% of farm area, equal to 701 ha (range 55 – 4400 ha) was allocated to the goat enterprise. Commercial producers used Boer bucks, at an average mating rate of 2.2%, over Boer X or feral X does. Weaning rates averaged 99% (range 51 - 165%). There was a large range in husbandry ($0 - $3.07) and supplementary feeding ($6.75 - $9.60) expenditure. Fifty percent of producers indicated that they carried out regular faecal egg counts to assess worm burdens. Seasonal supply patterns showed that producers were supplying Christmas and Easter markets with a live weight range of 12 - 40 kg and an overall average live weight of 26 kg. The issues of concern identified by commercial growers were: internal parasitism, doe fertility, kid predation, kid growth rates, Johnes disease, and fencing security. This study indicated that there is considerable scope to improve the productivity of the Victorian commercial goat meat industry. Most producers supply smaller carcases on a strong seasonal basis. This supply pattern inhibits industry development.

A supply chain approach to meat goat industry development

The Victorian goat meat industry is a significant contributor to export earnings, which is derived largely from the harvest of feral goats. The potential for exports of farmed goat meat into Asian product markets is being developed in a supply chain approach with producers, processors, exporters and Asian importers. Producers have been networked in four locations to improve supply capability and participate in production and economic benchmarking. In the absence of an existing market for premium farmed goat meat, a larger group of producers are cooperating with a marketer to develop a niche market in the Asian food service sector. This presents a challenge to the group in developing commercial relationships and playing a role in the marketing of their goat meat.