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DBSCAN CLUSTERING USING JAVA
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Density based spatial clustering noise (DBSCAN) applications are the 1996 data clustering algorithms of Ma butyl, Hans Peter Kliger, J ü RG sander and Xiao Weixu. [1] It is a density based clustering algorithm because it will find the number of corresponding nodes in the cluster from the estimated density distribution. DBSCAN is one of the most commonly used clustering algorithms, and it is also cited in the most scientific literature. ]Optics can be regarded as a generalization of DBSCAN, which can effectively replace the maximum search radius and the ε parameter. I used the practical Java algorithm, which I have entered from the console itself.
bharatpk 2 0
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Calculator using C#
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Just create a calculator in a very easy way .i have created a calculator which look exactly like windows calculator and it works very good . i have used c# as the implemented language for the same . in this code i have made use of Listeners to get the input from the buttons click its quite easy trick .
bharatpk 1 0
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QR code generation using asp.net
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QR code (abbreviated from Quick Response Code) is the trademark for a type of matrix barcode (or two-dimensional barcode) first designed for the automotive industry in Japan. A barcode is a machine-readable optical label that contains information about the item to which it is attached. A QR code uses four standardized encoding modes (numeric, alphanumeric, byte / binary, and kanji) to efficiently store data; extensions may also be used.
bharatpk 0 0
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K means Algorithm in cluster analysis
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k-means clustering is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. The problem is computationally difficult (NP-hard); however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data; however, k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes. In our project we are implementing K means for Blood sample data set and we are going to preprocess the data set to remove the null values and unwanted attrib
bharatpk 0 0
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