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What is Self-Organizing Map

Handbook of Research on Contemporary Perspectives on Web-Based Systems
The self-organizing map (SOM) is a well-known neural network and certainly one of the most popular unsupervised learning algorithm. Since its invention by Finnish Professor Teuvo Kohonen in the early 1980s, more than 4000 research articles have been published on the algorithm, its conception, and uses. The SOM mapping is preserving, namely the most similar two data samples are in the input space, and the closer they will appear together on the final displayed map. This allows the user to identify clusters such as large sets of a specific type of input pattern.
Published in Chapter:
Dynamic Quota Calculation System (DQCS): Pricing and Quota Allocation of Telecom Customers via Data Mining Approaches
Ulaş Çelenk (Innova IT Solutions Inc., Turkey), Duygu Çelik Ertuğrul (Eastern Mediterranean University, North Cyprus), Metin Zontul (Istanbul Aydin University, Turkey), Atilla Elçi (Hasan Kalyoncu University, Turkey), and Osman Nuri Uçan (Istanbul Kemerburgaz University, Turkey)
DOI: 10.4018/978-1-5225-5384-7.ch019
Abstract
One of the most important IT sectors that requires big data management is mobile data communication systems (MDCS) of GSM companies. In the charging mechanism of current MDCS, a subscriber “surfs” on the internet that creates data traffic and a counter subtracts the amount of data used by the user from the subscriber's quota. In other words, instant constant quota values are assigned to subscribers without concern for their previous amount of internet usage in current MDCS. Moreover, constant quota values cause constant charge calls in control traffic that are repeated for all new quota requests. Thus, performance degradation occurs because of the repetition of quota request calls and allocations. In this chapter, a dynamic quota calculation system (DQCS) is proposed for dynamic quota allocations and charging in MDCS using data mining approaches as two cascaded blocks. The first block is self-organizing map (SOM) clustering based on a sliding window (SW) methodology followed by the second block, which is the markov chain (MC); the overall system is denoted as “SOM/SW and MC.”
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Artificial Intelligence Applied: Six Actual Projects in Big Organizations
Neural network which simulated some cerebral functions in elaborating visual information. It is usually used to classify a large amount of data.
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Crime Profiling System
A well-known and classic clustering technique.
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Artificial Neural Networks in Physical Therapy
ANN used for visualizing low-dimensional views of high-dimensional data.
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Growing Self-Organizing Maps for Data Analysis
A subtype of artificial neural network. It is trained using unsupervised learning to produce low dimensional representation of the training samples while preserving the topological properties of the input space
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