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.

Recycling textiles : The use of ionic liquids in the separation of cotton polyester blends

Textiles are commonly made from intimate blends of polyester and cotton, which makes recycling very difficult. We report for the first time the use of ionic liquid in the separation of polyester cotton blends. By selective dissolution of the cotton component, the polyester component can be separated and recovered in high yield. This finding presents an environmentally benign approach to recycling textile waste. © 2014 The Royal Society of Chemistry.

India: real estate development in the fastest growing free market democracy

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Visible and UV functionality of TiO2 ternary nanocomposites on cotton

This research intends to increase the photocatalytic efficiency of cotton fabrics coated with TiO2-based nanocomposites under illumination particularly visible light. The fabrics were functionalized using a low-temperature sol-gel method of TiO2/Metal/SiO2 nanocomposite systems. Integrating silica and noble metals into TiO2 sol was put forth for boosting its functionality. Three noble metals (gold (Au), platinum (Pt) and silver (Ag)) with four different concentrations were employed. The photocatalytic activity of the functionalized fabrics was assessed through coffee stain-removal test and photodecomposition of methylene blue (MB) under UV and visible light. The impact of coating layers on fabrics' hydrophilicity was analyzed through measuring the water contact angle as well as the water absorption time. The fabrics were characterized using XRD, SEM and EDS. It was corroborated that the presence of silica enhanced the self-stain-removal capability of fabrics under UV. Moreover, the self-cleaning property of fabrics improved under both UV and visible light after integrating the metals into the colloids. In the same line, the self-cleaning activity threshold of fabrics was shifted to visible region.

Identify kinetic features of fibers growing, branching, and bundling in microstructure engineering of crystalline fiber network

Fibers growing, branching, and bundling are essential for the development of crystalline fiber networks of molecular gels. In this work, for two typical crystalline fiber networks, i.e. the network of spherulitic domains and the interconnected fibers network, related kinetic information is obtained using dynamic rheological measurements and analysis in terms of the Avrami theory. In combination with microstructure characterizations, we establish the correlation of the Avrami derived kinetic parameter not only with the nucleation nature and growth dimensionality of fibers and branches, but also with the fiber bundles induced by fiber-fiber interactions. Our study highlights the advantage of simple dynamic rheological measurements over other spectroscopic methods used in previous studies for providing more kinetic information on fiber-fiber interactions, enabling the Avrami analyses to extract distinct kinetic features not only for fibers growing and branching, but also for bundling in the creation of strong interconnected fibers networks. This work may be helpful for the implementation of precise kinetic control of crystalline fiber network formations for achieving desirable microstructures and rheological properties for advanced applications of gel materials. This journal is © the Partner Organisations 2014.