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In this work it was applied the Data Mining for the first time, to the information generated by the Permanent Survey of Homes of the year 2009, of the Social Observatory of the University of the Litoral in Santa Fe, Argentina. The objective was to use classification algorithms that possess descriptive focuses and it is one of the tasks more used in Data Mining, to obtain new knowledge on the family of Santa Fe. The database was made with the information of the Permanent Survey of Homes of the year 2009 and it was processed with the software of data mining Weka 3.6.2. The classification algorithms that better results contributed were: Ridor, and J48, belonging to the groups of Rules and Trees of Decision, being the classification algorithm J48 the one that better it classifies to the Permanent Survey of Homes 2009, of the Social Observatory of the National University of the Litoral. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to solve instances of the TSP. This method is comparable in effectiveness and efficiency with "nearest neighbour", "cheapest insertion", "two-way exchange improvement" and "branch and bound". The first section provides a literature review of the combinatorial optimization, the second provides a reference frame, the third the methodology used and the fourth contains, inter alia, system thinking, A×B factorial design and management tool CAP-DO. The fourth section presents the design "new" method called systemic tetraheuristic, followed by ANOVA and Duncan ranges for each factor: method and number of cities; this section concludes with an analysis of the proportion of "failures" of the proposed algorithm with increasing complexity of the TSP. As a result of this study a method is presented for solving the TSP. This method consists of: 1. "nearest neighbour", 2. "short-term sacrifice" 3. "LIFO transfer" and a support heuristic "right search 4P4 ". For the design of the "sacrifice short-term" procedure, it was necessary to analyze the "nearest neighbour" heuristic from a systems perspective. This heuristic is related to the "quick solutions that fail" archetype, which can be applied to day-to-day. The proposed method has proved to be more efficient than others, in what concerns quality of the solution and the computation time, especially when the complexity of the TSP increases. [ABSTRACT FROM AUTHOR]