Fuzzy association rule mining from spatio-temporal data: an analysis of meteorological data in turkey

Download

index.pdf

Date

2008

Author

Ünal Çalargün, Seda

Metadata

Show full item record

Item Usage Stats

76
views

19
downloads

Data mining is the extraction of interesting non-trivial, implicit, previously unknown and potentially useful information or patterns from data in large databases. Association rule mining is a data mining method that seeks to discover associations among transactions encoded within a database. Data mining on spatio-temporal data takes into consideration the dynamics of spatially extended systems for which large amounts of spatial data exist, given that all real world spatial data exists in some temporal context. We need fuzzy sets in mining association rules from spatio-temporal databases since fuzzy sets handle the numerical data better by softening the sharp boundaries of data which models the uncertainty embedded in the meaning of data. In this thesis, fuzzy association rule mining is performed on spatio-temporal data using data cubes and Apriori algorithm. A methodology is developed for fuzzy spatio-temporal data cube construction. Besides the performance criteria interpretability, precision, utility, novelty, direct-to-the-point and visualization are defined to be the metrics for the comparison of association rule mining techniques. Fuzzy association rule mining using spatio-temporal data cubes and Apriori algorithm performed within the scope of this thesis are compared using these metrics. Real meteorological data (precipitation and temperature) for Turkey recorded between 1970 and 2007 are analyzed using data cube and Apriori algorithm in order to generate the fuzzy association rules.

Subject Keywords

Computer enginnering.

URI

http://etd.lib.metu.edu.tr/upload/12609308/index.pdf
https://hdl.handle.net/11511/17532

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Data mining on architecture simulation Maden, Engin; Karagöz, Pınar; Department of Computer Engineering (2010) Data mining is the process of extracting patterns from huge data. One of the branches in data mining is mining sequence data and here the data can be viewed as a sequence of events and each event has an associated time of occurrence. Sequence data is modelled using episodes and events are included in episodes. The aim of this thesis work is analysing architecture simulation output data by applying episode mining techniques, showing the previously known relationships between the events in architecture and pr...
A hybrid movie recommender using dynamic fuzzy clustering Gürcan, Fatih; Birtürk, Ayşe Nur; Department of Computer Engineering (2010) Recommender systems are information retrieval tools helping users in their information seeking tasks and guiding them in a large space of possible options. Many hybrid recommender systems are proposed so far to overcome shortcomings born of pure content-based (PCB) and pure collaborative fi ltering (PCF) systems. Most studies on recommender systems aim to improve the accuracy and efficiency of predictions. In this thesis, we propose an online hybrid recommender strategy (CBCFdfc) based on content boosted co...
Multi-resolution visualization of large scale protein networks enriched with gene ontology annotations Yaşar, Sevgi; Can, Tolga; Department of Computer Engineering (2009) Genome scale protein-protein interactions (PPIs) are interpreted as networks or graphs with thousands of nodes from the perspective of computer science. PPI networks represent various types of possible interactions among proteins or genes of a genome. PPI data is vital in protein function prediction since functions of the cells are performed by groups of proteins interacting with each other and main complexes of the cell are made of proteins interacting with each other. Recent increase in protein interactio...
Using semantic web services for data integration in banking domain Okat, Çağlar; Doğru, Ali Hikmet; Department of Computer Engineering (2010) A semantic model oriented transformation mechanism is developed for the centralization of intra-enterprise data integration. Such a mechanism is especially crucial in the banking domain which is selected in this study. A new domain ontology is constructed to provide basis for annotations. A bottom-up approach is preferred for semantic annotations to utilize existing web service definitions. Transformations between syntactic web service XML responses and semantic model concepts are defined in transformation ...
Efficient index structures for video databases Açar, Esra; Yazıcı, Adnan; Department of Computer Engineering (2008) Content-based retrieval of multimedia data has been still an active research area. The efficient retrieval of video data is proven a difficult task for content-based video retrieval systems. In this thesis study, a Content-Based Video Retrieval (CBVR) system that adapts two different index structures, namely Slim-Tree and BitMatrix, for efficiently retrieving videos based on low-level features such as color, texture, shape and motion is presented. The system represents low-level features of video data with ...

Citation Formats

S. Ünal Çalargün, “Fuzzy association rule mining from spatio-temporal data: an analysis of meteorological data in turkey,” M.S. - Master of Science, Middle East Technical University, 2008.