Data Mining ⛏️ - Methoden in der Simulation

Data Mining ⛏️ - Methoden in der Simulation

👓 Enrico Seib
Data Mining ⛏️ - Methoden in der Simulation

Data Mining ⛏️ - Methoden in der Simulation

✅ Bachelorarbeit aus dem Jahr 2️⃣0️⃣0️⃣8️⃣ im Fachbereich Informatik - Wirtschaftsinformatik, Note 🎵: 1️⃣,0️⃣, Universität Rostock (Institut für Informatik, Lehrstuhl für Modellierung und Simulation), 100 💯 Quellen im Literaturverzeichnis, Sprache: Deutsch, Abstract: Principles and ➕ methods of data mining ⛏️ are a widespread area, i.e. retail dealer use data mining ⛏️ tools 🔪 to analyze the behavior of customers, computer 💻️ hardware supplier use data mining ⛏️ to optimize their inventory. There are multiple possibilities of using data mining ⛏️ techniques, even in technical and ➕ scientific areas of applications. In regard of manyfold fields 🏑 of application, there are no less than the number of techniques and ➕ methods for Data Mining ⛏️ in existence. Another field 🏑 to apply Data Mining ⛏️ technique is the domain of simulation. Simulation is the computer 💻️-based approach of executing and ➕ experimenting of and ➕ with models. One 1️⃣ aim of this thesis is to analyze data mining ⛏️ tools 🔪 to 👀 see how capable they are solving data mining ⛏️ duties with respect to data calculated by simulation. Different data mining ⛏️ tools 🔪 are analyzed, commercial tools 🔪 like SPSS and ➕ SPSS Clementine as well as established and ➕ freely available tools 🔪 like WEKA and ➕ the R-Project. These tools 🔪 are analyzed in matters of their data mining ⛏️ functionalities, options to access ♿️ different data sources, and ➕ their complexity of different data mining ⛏️ algorithms. Beyond the analysis of data mining ⛏️ tools 🔪 with respect to functionality and ➕ simulation, envi-ronments for modeling a...



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