Geometric clustering using the information bottleneck method

We argue that K–means and deterministic annealing algorithms for geometric clustering can be derived from the more general Information Bottleneck approach. If we cluster the identities of data points to preserve information about their location, the set of optimal solutions is massively degenerate. But if we treat the equations that define the optimal solution as an iterative algorithm, then a set of “smooth ” initial conditions selects solutions with the desired geometrical properties. In addition to conceptual unification, we argue that this approach can be more efficient and robust than classic algorithms.

Continuous Iterative Guided Spectral Class Rejection Classification Algorithm: Part 1

This paper outlines the changes necessary to convert the iterative guided spectral class rejection (IGSCR) classification algorithm to a soft classification algorithm. IGSCR uses a hypothesis test to select clusters to use in classification and iteratively refines clusters not yet selected for classification. Both steps assume that cluster and class memberships are crisp (either zero or one). In order to make soft cluster and class assignments (between zero and one), a new hypothesis test and iterative refinement technique are introduced that are suitable for soft clusters. The new hypothesis test, called the (class) association significance test, is based on the normal distribution, and a proof is supplied to show that the assumption of normality is reasonable. Soft clusters are iteratively refined by creating new clusters using information contained in a targeted soft cluster. Soft cluster evaluation and refinement can then be combined to form a soft classification algorithm, continuous iterative guided spectral class rejection (CIGSCR)

ANALISA PENGARUH LAMPU JALAN TERHADAP INDEKS TINGKAT PELAYANAN JALAN DENGAN PERBANDINGAN METODE GREENSHIELD DAN METODE GREENBERG

This study aims to analyze the index level of service during the day and evening using street lights. Index level of service is analized with Greenshield method and Greenberg method, implemented in good condition or smooth road pavement, freeway, a few turn, and has a street light which function optimally. Thus do the analysis of vehicle speed in the afternoon and evening, and by using both methods can be analyzed index level of servic

Clustering constrained by dependencies

Clustering is the unsupervised method of grouping data samples to form a partition of a given dataset. Such grouping is typically done based on homogeneity assumptions of clusters over an attribute space and hence the precise definition of the similarity metric affects the clusters inferred. In recent years, new formulations of clustering have emerged that posit indirect constraints on clustering, typically in terms of preserving dependencies between data samples and auxiliary variables. These formulations find applications in bioinformatics, web mining, social network analysis, and many other domains. The purpose of this survey is to provide a gentle introduction to these formulations, their mathematical assumptions, and the contexts under which they are applicable