Analysis of air quality data in Mexico city with clustering techniques based on genetic algorithms

Data analysis is extremely important, because through this process we can infer knowledge. Clustering is a technique for analyzing features, where there are not defined groups. This technique allows us to analyze the behavior of the information and characteristics by using a similarity measure. However, for processing large amounts of data, the use of classical clustering techniques is time consuming. For this reason is necessary to propose hybrid algorithms that combine computational strategies in order to find an optimal solution. An optimization strategy used frequently by the community is the genetic algorithms, this technique is inspired on the evolutionary theory.