Search results for "K-Means"
showing 3 items of 43 documents
District metered area design through multicriteria and multiobjective optimization
2022
[EN] The design of district metered areas (DMA) in potable water supply systems is of paramount importance for water utilities to properly manage their systems. Concomitant to their main objective, namely, to deliver quality water to consumers, the benefits include leakage reduction and prompt reaction in cases of natural or malicious contamination events. Given the structure of a water distribution network (WDN), graph theory is the basis for DMA design, and clustering algorithms can be applied to perform the partitioning. However, such sectorization entails a number of network modifications (installing cut-off valves and metering and control devices) involving costs and operation changes,…
Joint interpretation of seismic refraction tomography and electrical resistivity tomography by cluster analysis to detect buried cavities
2020
Abstract In the last few years, the geophysical methods of seismic refraction tomography (SRT) and electrical resistivity tomography (ERT) are among the most used geophysical techniques for the reconstruction of subsoil geometries, for the investigation of underground cavities and also for the archaeological prospecting. However, the main disadvantage of each geophysical method is the difficulty of final interpretation of the data. In order to eliminate artifacts and generally improve the reliability and accuracy of geophysical interpretation, it is useful to perform a joint approach of different geophysical methods, also introducing the a priori information. In this work, it is shown the i…
Improvements and applications of the elements of prototype-based clustering
2018
Clustering or cluster analysis is an essential part of data mining, machine learning, and pattern recognition. The most popularly applied clustering methods are partitioning-based or prototype-based methods. Prototype-based clustering methods usually have easy implementability and good scalability. These methods, such as K-means clustering, have been used for different applications in various fields. On the other hand, prototype-based clustering methods are typically sensitive to initialization, and the selection of the number of clusters for knowledge discovery purposes is not straightforward. In the era of big data, in high-velocity, ever-growing datasets, which can also be erroneous, outl…