A KDSM methodology for analyzing repeated very short-serial measures in a psychiatric ill-structured domain is introduced. This methodology is based on a combination of clustering based on rules with some Inductive Learning (Artificial Intelligence) and clustering (Statistics) techniques. This proposal focuses on results obtained on a real application of this kind of data, where common statisti...

A new methodology for analyzing repeated very short-serial measurement on time in a medical ill-structured domain is introduced. This methodology is based on a combination of clustering based on rules with some Inductive Learning (AI) and clustering (Statistics) techniques. This proposal focuses on results obtained on a real application of this kind of data, where common statistical analysis (t...

Using historical time-series data, we test for convergence and common trends in real per capita output for New Zealand and her four major trading partners. Both bivariate and multivariate time-series methods are used, and we also implement the fuzzy c-means clustering algorithm as an alternative basis for detecting convergence. The results of our time-series analysis accord with earlier studies...

Efficient analysis (including segmentation and classification) of multivariate data is an inherently complex task in which features occur as salient members of clusters in a multi-dimensional data space. The clusters assume a variety of distributions and frequently overlap, leading to difficulty in segmentation and classification. In many cases, similar but distinct features in the dataset are ...

An approach to automatic detection of syllable structure is presented. We demonstrate a novel application of EM-based clustering to multivariate data, exempli ed by the induction of 3and 5-dimensional probabilistic syllable classes. The qualitative evaluation shows that the method yields phonologically meaningful syllable classes. We then propose a novel approach to grapheme-to-phoneme conversi...

We illustrate unsupervised clustering algorithms using a twodimensional color-magnitude diagram constructed from the COMBO-17 (`Classifying Objects by Medium-Band Observations in 17 Filters') photometric survey of normal galaxies (Wolf et al. 2003). The R script below starts with the which function to filter the dataset, keeping only low-redshift galaxies with z < 0.3 and remove a few points wi...

When functional data come as multiple curves per subject, characterizing the source of variations is not a trivial problem. The complexity of the problem goes deeper when there is phase variation in addition to amplitude variation. We consider clustering problem with multivariate functional data that have phase variations among the functional variables. We propose a conditional subject-specific...