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Applying fuzzy logic to clustering techniques leads to more robust and autonomous methods like the fuzzy joint points (FJP) which is a density based fuzzy clustering algorithm that requires no parameters to be set. However, a straightforward implementation of the method is rather slow. Recently, a faster but parameter dependent version of the algorithm was(More)
Since notion of smart city and related applications have become very common recently, local governments have an urge to use computers and high technology in their public transportation systems. A typical example is planning optimal journeys for travelers depending on different parameters. In this work, a graph model and a solution method based on well-known(More)
The fuzzy joint points (FJP) is a method that uses a fuzzy neighborhood notion to deal with neighborhood parameter selection issue of classical density-based clustering and offers an unsupervised clustering tool. Recent works improved the method in terms of speed to enable the method for big data applications. However, space efficiency of the method is(More)
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