Md Anisur Rahman

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In this paper we present a novel clustering technique called Seed-Detective. It is a combination of modified versions of two existing techniques namely Ex-Detective and Simple K-Means. Seed-Detective first discovers a set of preliminary clusters using our modified Ex-Detective. The modified Ex-Detective allows a data miner to assign different weights(More)
We present a novel fuzzy clustering technique called CRUDAW that allows a data miner to assign weights on the attributes of a data set based on their importance (to the data miner) for clustering. The technique uses a novel approach to select initial seeds deterministically (not randomly) using the density of the records of a data set. CRUDAW also selects(More)
Many existing clustering techniques including K-Means require a user input on the number of clusters. It is often extremely difficult for a user to accurately estimate the number of clusters in a data set. The genetic algorithms (GAs) generally determine the number of clusters automatically. However, they typically choose the genes and the number of genes(More)
Both safety and the capacity of the roadway system are highly dependent on the car-following characteristics of drivers. Car-following theory describes the driver behavior of vehicles following other vehicles in a traffic stream. In the last few decades, many car-following models have been developed; however, studies are still needed to improve their(More)
In this paper we present two clustering techniques called ModEx and Seed-Detective. ModEx is a modified version of an existing clustering technique called Ex-Detective. It addresses some limitations of Ex-Detective. Seed-Detective is a combination of ModEx and Simple K-Means. Seed-Detective uses ModEx to produce a set of high quality initial seeds that are(More)
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