We investigate the use of fuzzy clustering for the analysis of software metrics databases. Software metrics are collected at various points during software development, in order to monitor and control the quality of a software product. We use fuzzy clustering to examine three collections of software metrics. This is one of the very few attempts to use unsupervised learning in the software metri...

This paper discusses the unsupervised learning problem. An important part of the unsu-pervised learning problem is determining the number of constituent groups (components or classes) which best describes some data. We apply the Minimum Message Length (MML) criterion to the unsupervised learning problem , modifying an earlier such MML application. We give an empirical comparison of criteria pro...

English. We propose a system to extract entities and relations from a set of clinical records in Italian based on two preceding works (Alicante et al., 2016b) and (Alicante et al., 2016a). This approach does not require annotated data and is based on existing domain lexical resources and unsupervised machine learning techniques. Italiano. Proponiamo un sistema per estrarre entità e relazioni da...

This paper describes a two-stage system for the recognition of postage meter values. A feed-forward Neural Abstraction Pyramid is initialized in an unsupervised manner and trained in a supervised fashion to classify an entire digit block. It does not need prior digit segmentation. If the block recognition is not confident enough, a second stage tries to recognize single digits, taking into acco...

2 Clustering Techniques: A Brief Survey 4 2.1 Partitional Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Hierarchical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3 Discriminative vs. Generative Models . . . . . . . . . . . . . . . . . 12 2.4 Assessment of Results . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4.1 Internal (model-based, unsup...

The basic premise of vibration-based damage detection is that damage will significantly alter the stiffness, mass, or energy dissipation properties of a system, which, in turn, alter the measured dynamic response of the system. Although the basis for vibration-based damage detection appears intuitive, its actual application poses many significant technical challenges. A fundamental challenge is...

Article history: Received 1 December 2007 Received in revised form 5 June 2008 Accepted 16 July 2008

An approach is proposed for robust online behaviour recognition and abnormality detection based on discovering natural grouping of bebaviour patterns through unsupervised learning and a time accumulative reliability measure. A novel behaviour learning model and a run-time accumulative reliability measure are introduced to determine both the natural groupings of possible normal behaviour classes...