Applying Fuzzy Data Mining to Tourism Area

Applying Fuzzy Data Mining to Tourism Area

R. A. Carrasco (Universidad de Granada, Spain), F. Araque (Universidad de Granada, Spain), A. Salguero (Universidad de Granada, Spain) and M. A. Vila (Universidad de Granada, Spain)
Copyright: © 2008 |Pages: 22
DOI: 10.4018/978-1-59904-853-6.ch022
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Abstract

Soaring is a recreational activity and a competitive sport where individuals fly un-powered aircrafts known as gliders. The soaring location selection process depends on a number of factors, resulting in a complex decision-making task. In this chapter, we propose the use of an extension of the FSQL language for fuzzy queries as one of the techniques of data mining that can be used to solve the problem of offering a better place for soaring given the environmental conditions and customer characteristics. The FSQL language is an extension of the SQL language that permits us to write flexible conditions in our queries to a fuzzy or traditional database. After doing a process of clustering and characterization of a large customer database in a data warehouse, we are able of classify the next clients in a cluster and offer an answer according to it.

Key Terms in this Chapter

Centroid: The centroid of a cluster is the average point in the multidimensional space defined by the dimensions. In a sense, it is the center of gravity for the respective cluster.

Data Warehouse: It is the storage of consolidated information dedicated to easily and quickly provide simple or preaggregated data for analysis.

Clustering: It is identifying elements with similar characteristics, and grouping cases with similar characteristics together.

Functional dependency: In the regular case, a functional dependency, denoted by X? Y, expresses that a function exists between the two sets of attributes X and Y, and it can be stated as follows: For any pair of tuples t1 and t2, if t1 and t2 have an equal value on X, they also have the same value on Y.

Ultrametric Matrix: A matrix D is an ultrametric matrix if there exists a rooted tree T with function height(v) = 0 defined for each node v ? V (T) (height(v) = Di,j, where v is the most recent common ancestor of i and j) such that whenever v > w (i.e., v is on the path from w to the root of T), we have height(v) > height(w).

Classif ication: Classification is the systematic arrangement of elements based on everything we know about them.

Monotonicity: Functions between ordered sets are monotonic (or monotone) if they preserve the given order.

Characterization: It the act of describing distinctive characteristics or essential features.

Dendrogram: It is a graphical procedure for representing the output of a hierarchical clustering method. A dendrogram is strictly defined as a binary tree with a distinguished root that has all the data items as its leaves. Conventionally, all the leaves are shown at the same level of the drawing. The ordering of the leaves is arbitrary, as is their horizontal position. The heights of the internal nodes may be arbitrary, or may be related to the metric information used to form the clustering.

Decision Support System: A decision support system is a highly flexible and interactive IT system designed to support decision making when the problem is not structured.

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