Jorge Rodas

KDSM Methodology for Knowledge Discovery from Ill-Structured Domains Presenting Very Short and Repeated Serial Measures with Blocking Factor

This paper is an introduction of Knowledge Discovery in Serial Measurement (KDSM) methodology for analyzing repeated and very short serial measures with a blocking factor in ill-structured domains (ISD). KDSM arises from the results obtained in a real application of psychiatry (presented in the previous issue of CCIA [11]). In this application domain, common statistical analysis (time series analysis, multivariate data analysis...) and artificial intelligence techniques (knowledge based methods, inductive learning), employed independently, are often inadequate due to the intrinsic characteristics of ISD. KDSM is based on both the combination of statistical methods and artificial intelligence techniques, including the use of clustering based on rules (introduced by Gibert in 1994).

Knowledge discovery in repeated very short serial measurements with a blocking factor. Application to a psychiatric domain

A new hybrid methodology for Knowledge Discovery in Serial Measurement (KDSM) and the results of applying it to psychiatry are presented in this paper. In the application domain where serial measurements are repeated and very short (i.e. very few parameters), traditional measuremethods for series analysis are inappropriate. Moreover, some information is non-serial but is closely connected to serial measurements. For this reason, common statistical analysis (time series analysis, multivariate data analysis ...) and artificial intelligence techniques (knowledge based methods, inductive learning) used independently provide often poor results because of the characteristics above and it is necessary a suitable way of analyzing these situations. KDSM is built as an hybrid methodology, specially designed to obtain knowledge from repeated very short serial measurement, in order to overcome the limitations of Artificial Intelligence or Statistics techniques. Novel knowledge about electroconvulsive therapy behavior was obtained once KDSM was applied to this specific domain. Thus, KDSM gives a possible solution to a knowledge problem.

Understanding the Consequences of Social Isolation Using Fireworks Algorithm

Social isolation, also known as “social withdrawal” occurs when a person is away from their environment completely involuntarily but might think otherwise. This condition occurs in people of all ages and can be a result of traumatic events in their history, such as being the victim of bullying or as part of any medical condition, such as depression. In this research we try to explain this social concept using a novel Bioinspired Algorithm named Firework Algorithm. Four minority groups in Chihuahua, have high levels of social isolation, thereby generating between different situations, principally school drop due to the lack of equal opportunities for the majority group. This research seeks to elucidate the reasons why it happens this by simulating social behavior. As future work, the research will be replicated in Aguascalientes.

Using the KDSM methodology for knowledge discovery from a labor domain

The paper presents the knowledge discovery in serial measures (KDSM) methodology as an easy and optimal way for analyzing repeated very short serial measures with a blocking factor. An application to labor the domain is described using KDSM. A novel knowledge about labor domains behavior was obtained once KDSM was applied to this specific domain. KDSM is a hybrid methodology (statistic and artificial intelligence) that gives a possible solution to a knowledge problem, especially when seemingly there are no relevant attributes.

Electroshock Effects Identification Using Classification Based on Rules

This article focuses on results obtained from a hierarchical classification applied to a repeated short time series data in a medical ill-structured domain. The analyzed information is relative to patients -with major depressive disorders or schizophrenia- under ECT treatment; as a consequence, this information contains data corresponding to measures taken at different time, throughout a 24-hour period after an electroshock application to the patient.