Structure in the 3D Galaxy Distribution: I. Methods and Example Results

Three methods for detecting and characterizing structure in point data, such as that generated by redshift surveys, are described: classification using self-organizing maps, segmentation using Bayesian blocks, and density estimation using adaptive kernels. The first two methods are new, and allow detection and characterization of structures of arbitrary shape and at a wide range of spatial scales. These methods should elucidate not only clusters, but also the more distributed, wide-ranging filaments and sheets, and further allow the possibility of detecting and characterizing an even broader class of shapes. The methods are demonstrated and compared in application to three data sets: a carefully selected volume-limited sample from the Sloan Digital Sky Survey redshift data, a similarly selected sample from the Millennium Simulation, and a set of points independently drawn from a uniform probability distribution -- a so-called Poisson distribution. We demonstrate a few of the many ways in which these methods elucidate large scale structure in the distribution of galaxies in the nearby Universe.Comment: Re-posted after referee corrections along with partially re-written introduction. 80 pages, 31 figures, ApJ in Press. For full sized figures please download from: http://astrophysics.arc.nasa.gov/~mway/lss1.pd

Music Mood Player Implementation Applied in Daycare Using Self Organizing Map Method

. Music is an art, entertainment and human activity that involve some organized sounds. Music is closely related to human psychology. A piece of music often associated with certain adjectives such as happy, sad, romantic and many more. The linkage between the music with a certain mood has been widely used in various occasions by people, there for music classification based on relevance to a particular emotion is important. Daycare is one example of an institution that used music as therapy or tools of support in each of its parenting activities. This research concerns in implementation of a music mood player using Self Organizing Map applied at the Daycare. The features that are used on this music mood player are rhythm patterns of the music. The mood parameters that used in this system is based on Robert Thayer\u27s energy-stress model which are exuberance / happy, contentment / relax, anxious and depression. The system is tested using a set of songs with various genres and the classification results are compared with the mood obtained by child psychology expert. The system can be set automatically according to the activities at daycare.

SOMvisua: Data Clustering and Visualization Based on SOM and GHSOM

Text in web pages is based on expert opinion of a large number of people including the views of authors. These views are based on cultural or community aspects, which make extracting information from text very difficult. Search in text usually finds text similarities between paragraphs in documents. This paper proposes a framework for data clustering and visualization called SOMvisua. SOMvisua is based on a graph representation of data input for Self-Organizing Map (SOM) and Growing Hierarchically Self-Organizing Map (GHSOM) algorithms. In SOMvisua, sentences from an input article are represented as graph model instead of vector space model. SOM and GHSOM clustering algorithms construct knowledge from this article

Visual-Interactive Analysis With Self-Organizing Maps - Advances and Research Challenges

Based on the Self-Organizing Map (SOM) algorithm, development of effective solutions for visual analysis and retrieval in complex data is possible. Example application domains include retrieval in multimedia data bases, and analysis in financial, text, and general high-dimensional data sets. While early work defined basic concepts for data representation and visual mappings for SOM-based analysis, recent work contributed advanced visual representations of the output of the SOM algorithm, and explored innovative application concepts

Component Selection for the Metro Visualisation of the Self-Organising Map

Self-Organising Maps have been used for a wide range of clustering applications. They are well-suited for various visualisation techniques to offer better insight into the clustered data sets. A particularly feasible visualisation is the plotting of single components of a data set and their distribution across the SOM. One central problem of the visualisation of Component Planes is that a single plot is needed for each component; this understandably leads to problems with higher-dimensional data. We therefore build on the Metro Visualisation for Self-Organising Maps which integrates the idea of Component Planes into one illustration. Higher-dimensional data sets still pose problems in terms of overloaded visualisations - component selection and aggregation techniques are highly desirable. We therefore propose and compare two methods, one for the aggregation of correlated components, one for the selection of the components most feasible for visualisation for a given clustering

Exploring Music Collections by Browsing Different Views

The availability of large music collections calls for ways to efficiently access and explore them. We present a new approach which uses descriptors derived from audio analysis and meta-information to create different views of a collection. Such views can have a focus on timbre, rhythm, artist, style or other aspects of music. For each view the pieces of music are organized on a map in such a way that similar pieces are located close to each other. The maps are visualized using an Islands of Music metaphor where islands represent groups of similar pieces. The different maps are linked to each other using a new technique to align Self-Organizing Maps. The user is able to browse the collection and explore different aspects by gradually changing focus from one view to another. We demonstrate our approach on a small collection using a user defined view and two views generated from audio analysis, namely, beat periodicity as an aspect of rhythm and spectral information as an aspect of timbre