How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?

In numerous applicative contexts, data are too rich and too complex to be represented by numerical vectors. A general approach to extend machine learning and data mining techniques to such data is to really on a dissimilarity or on a kernel that measures how different or similar two objects are. This approach has been used to define several variants of the Self Organizing Map (SOM). This paper reviews those variants in using a common set of notations in order to outline differences and similarities between them. It discusses the advantages and drawbacks of the variants, as well as the actual relevance of the dissimilarity/kernel SOM for practical applications

ASPECT: A spectra clustering tool for exploration of large spectral surveys

We present the novel, semi-automated clustering tool ASPECT for analysing voluminous archives of spectra. The heart of the program is a neural network in form of Kohonen's self-organizing map. The resulting map is designed as an icon map suitable for the inspection by eye. The visual analysis is supported by the option to blend in individual object properties such as redshift, apparent magnitude, or signal-to-noise ratio. In addition, the package provides several tools for the selection of special spectral types, e.g. local difference maps which reflect the deviations of all spectra from one given input spectrum (real or artificial). ASPECT is able to produce a two-dimensional topological map of a huge number of spectra. The software package enables the user to browse and navigate through a huge data pool and helps him to gain an insight into underlying relationships between the spectra and other physical properties and to get the big picture of the entire data set. We demonstrate the capability of ASPECT by clustering the entire data pool of 0.6 million spectra from the Data Release 4 of the Sloan Digital Sky Survey (SDSS). To illustrate the results regarding quality and completeness we track objects from existing catalogues of quasars and carbon stars, respectively, and connect the SDSS spectra with morphological information from the GalaxyZoo project.Comment: 15 pages, 14 figures; accepted for publication in Astronomy and Astrophysic

Median topographic maps for biomedical data sets

Median clustering extends popular neural data analysis methods such as the self-organizing map or neural gas to general data structures given by a dissimilarity matrix only. This offers flexible and robust global data inspection methods which are particularly suited for a variety of data as occurs in biomedical domains. In this chapter, we give an overview about median clustering and its properties and extensions, with a particular focus on efficient implementations adapted to large scale data analysis

An Evolving View of Saturn's Dynamic Rings

International audienceWe review our understanding of Saturn's rings after nearly 6 years of observations by the Cassini spacecraft. Saturn's rings are composed mostly of water ice but also contain an undetermined reddish contaminant. The rings exhibit a range of structure across many spatial scales; some of this involves the interplay of the fluid nature and the self-gravity of innumerable orbiting centimeter- to meter-sized particles, and the effects of several peripheral and embedded moonlets, but much remains unexplained. A few aspects of ring structure change on time scales as short as days. It remains unclear whether the vigorous evolutionary processes to which the rings are subject imply a much younger age than that of the solar system. Processes on view at Saturn have parallels in circumstellar disks

Swarm-Organized Topographic Mapping

Topographieerhaltende Abbildungen versuchen, hochdimensionale oder komplexe Datenbestände auf einen niederdimensionalen Ausgaberaum abzubilden, wobei die Topographie der Daten hinreichend gut wiedergegeben werden soll. Die Qualität solcher Abbildung hängt gewöhnlich vom eingesetzten Nachbarschaftskonzept des konstruierenden Algorithmus ab. Die Schwarm-Organisierte Projektion ermöglicht eine Lösung dieses Parametrisierungsproblems durch die Verwendung von Techniken der Schwarmintelligenz. Die praktische Verwendbarkeit dieser Methodik wurde durch zwei Anwendungen auf dem Feld der Molekularbiologie sowie der Finanzanalytik demonstriert

Defining level of service criteria of urban streets in Indian context

Speed ranges of Level of Service (LOS) categories of urban streets are not well defined for highly heterogeneous traffic flow condition on urban streets in Indian context. In this respect, a study was carried out in the city of Mumbai, India and the result was tested on two major corridors in Kolkata City. Average travel speed on street segments is used as the measure of effectiveness, which in this case has been derived from second by second speed data collected using Global Positioning System (GPS) receiver fitted on mobile vehicles. Hierarchical Agglomerative Clustering (HAC) is applied on average travel speeds to define the speed ranges of urban street and LOS categories. Applying this methodology it is found that urban street speed-ranges of LOS categories valid in Indian context are different from that values specified in HCM (2000). The application of this procedure is that in a simple manner with the application of GPS it can be applied in the evaluation of level of service of urban streets in different environment

Unsupervised Feature Extraction Techniques for Plasma Semiconductor Etch Processes

As feature sizes on semiconductor chips continue to shrink plasma etching is becoming a more and more critical process in achieving low cost high-volume manufacturing. Due to the highly complex physics of plasma and chemical reactions between plasma species, control of plasma etch processes is one of the most di±cult challenges facing the integrated circuit industry. This is largely due to the di±culty with monitoring plasmas. Optical Emission Spectroscopy (OES) technology can be used to produce rich plasma chemical information in real time and is increasingly being considered in semiconductor manufacturing for process monitoring and control of plasma etch processes. However, OES data is complex and inherently highly redundant, necessitating the development of advanced algorithms for e®ective feature extraction. In this thesis, three new unsupervised feature extraction algorithms have been proposed for OES data analysis and the algorithm properties have been explored with the aid of both arti¯cial and industrial benchmark data sets. The ¯rst algorithm, AWSPCA (AdaptiveWeighting Sparse Principal Component Analysis), is developed for dimension reduction with respect to variations in the analysed variables. The algorithm gener- ates sparse principle components while retaining orthogonality and grouping correlated variables together. The second algorithm, MSC (Max Separation Clustering), is devel- oped for clustering variables with distinctive patterns and providing e®ective pattern representation by a small number of representative variables. The third algorithm, SLHC (Single Linkage Hierarchical Clustering), is developed to achieve a complete and detailed visualisation of the correlation between variables and across clusters in an OES data set. The developed algorithms open up opportunities for using OES data for accurate pro- cess control applications. For example, MSC enables the selection of relevant OES variables for better modeling and control of plasma etching processes. SLHC makes it possible to understand and interpret patterns in OES spectra and how they relate to the plasma chemistry. This in turns can help engineers to achieve an in-depth under- standing of underlying plasma processes

Cross-domain self organizing maps

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (leaves 59-60).In this thesis, I present a method for organizing and relating events represented in two domains: the transition-space domain, which focuses on change and the trajectory-space domain, which focuses on movement along paths. Particular events are described in both domains, and each description is fed into a self organizing map. After these self organizing maps have been trained with enough events, the maps are clustered independently. Then, after the two self organizing maps are clustered, the clusters in the two maps are themselves clustered, creating links between trajectory descriptions and the transition descriptions. Thus, I provide a method for relating events seen in multiple perspectives. After training with 1914 different sentences about motion, my implemented system noted that particular motions along a path are highly correlated with particular transitions. For example, "the bird flew to the top of a tree" is part of a trajectory cluster that is highly correlated with a transition cluster in which a motion appears and a distance first decreases and finally disappears.by Estanislao L. Fidelholtz.M.Eng