Neutrosophic K-means for the analysis of earthquake data in Ecuador

Abstract

The occurrence of earthquakes may have catastrophic and devastating consequences for the inhabitants of the place where they occur. Some regions are characterized by the high frequency of this type of natural phenomenon. Such is the case of Ecuador, a country with a high seismic index due to its location in a subduction zone between the Pacific Plate and the South American Plate. Predictions in the behavior of earthquakes are a way of prevention that allows taking measures according to vulnerability. Although it is difficult to accurately predict the occurrence of an earthquake, there are dissimilar types of analysis to observe its behavior and patterns of occurrence. The nature of earthquakes and their monitoring variables usually make up large databases. For its processing and subsequent analysis of the results, it is convenient to use statistical techniques of Data Mining such as K-Means. In this work, the classic K-Means method is combined with Neutrosophy to improve the results obtained by taking into account the indeterminacy of such complex data sets and including the diversity of the data and its fluctuation, due to the proximity among the boundaries and their membership clusters.

Keywords

• k-means clustering
• neutrosophy
• earthquakes
• prediction
• vulnerability