Fuzzy Classification in Shipwreck Scatter Analysis

Fuzzy Classification in Shipwreck Scatter Analysis

Yauheni Veryha (ABB Corporate Research Center, Germany), Jean-Yves Blot (Portugal Institute of Archaeology, Portugal) and Joao Coelho (Portugal Institute of Archaeology, Portugal)
Copyright: © 2008 |Pages: 22
DOI: 10.4018/978-1-59904-853-6.ch020
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There are many well-known applications of fuzzy sets theory in various fields of science and technology. However, we think that the area of maritime archaeology did not attract enough attention from researchers of fuzzy sets theory in the last decades. In this chapter, we present examples of problems arising in shipwreck scatter analysis where fuzzy classification may be very useful. Using a real-world example of fragments of ceramics from an ancient shipwreck, we present an exemplary application of the fuzzy classification framework with SQL querying for data mining in archaeological information systems. Our framework can be used as a data mining tool. It can be relatively easily integrated with conventional relational databases, which are widely used in existing archaeological information systems. The main benefits of using our fuzzy classification approach include flexible and precise data analysis with userfriendly information presentation at the report generation phase.

Key Terms in this Chapter

Structured Query Language: Structured query language (SQL) is the most popular computer language used to create, modify, retrieve, and manipulate data from relational database management systems. The language has evolved beyond its original purpose to support object-relational database management systems. It is an ANSI/ISO standard.

Petrology: Petrology is a field of geology that focuses on the study of rocks and the conditions by which they form. There are three branches of petrology corresponding to the three types of rocks: igneous, metamorphic, and sedimentary. The word petrology itself comes from the Greek word petra, meaning rock. The word lithology once was approximately synonymous with petrography, but today lithology is essentially a subdivision of petrology focusing on macroscopic hand-sample or outcrop-scale descriptions of rocks.

Data Mining: Data mining, also called knowledge discovery in databases or knowledge discovery and data mining, is the process of automatically searching large volumes of data for patterns using tools such as classification, association rule mining, clustering, and so forth. Data mining is a complex topic, has links with multiple core fields such as computer science, and adds value to rich seminal computational techniques from statistics, information retrieval, machine learning, and pattern recognition.

Maritime Archaeology: Maritime archaeology (also known as marine archaeology) is a discipline that studies human interaction with the sea, lakes, and rivers through the study of vessels, shoreside facilities, cargoes, human remains, and submerged landscapes. One specialty is underwater archaeology, which studies the past through any submerged remains. Another specialty within maritime archaeology is nautical archaeology, which studies vessel construction and use.

Fuzzy Set Operations: A fuzzy set operation is an operation on fuzzy sets. These operations are generalizations of crisp set operations. There is more than one possible generalization. The most widely used operations are called standard fuzzy set operations. There are three operations: fuzzy complements, fuzzy intersections, and fuzzy unions.

Fuzzy Sets: Fuzzy sets are an extension of classical set theory and are used in fuzzy logic. In classical set theory, the membership of elements in relation to a set is assessed in binary terms according to a crisp condition: An element either belongs or does not belong to the set. By contrast, fuzzy set theory permits the gradual assessment of the membership of elements in relation to a set; this is described with the aid of a membership function.

Membership Function: The membership function of a fuzzy set is a generalization of the indicator function in classical sets. In fuzzy logic, it represents the degree of truth as an extension of valuation. Degrees of truth are often confused with probabilities; however, they are conceptually distinct because fuzzy truth represents membership in vaguely defined sets, not the likelihood of some event or condition.

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