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This paper presents a new heuristic for the data clustering problem. It comprises two parts. The first part is a greedy algorithm, which selects the data points that can act as the centroids of well-separated clusters. The second part is a single-solution-based heuristic, which performs clustering with the objective of optimizing a cluster validity index. Single-solution-based heuristics are memory...
The increasing amount of text documents in digital forms affect the text analysis techniques. Text clustering (TC) is one of the important techniques used for showing a massive amount of text documents by clusters. Hence, the main problem that affects the text clustering technique is the presence sparse and uninformative features on the text documents. The feature selection (FS) is an essential unsupervised...
K-Means has been paid attention to many areas recently, however, it is easy to fall into local optimum and the outliers influence the final results. This paper proposes an improved method for k-means clustering. Different from the traditional k-means algorithms, in our algorithm both intracluster compactness and intercluster separation are considered in our new presented method. A new model is established...
One of the challenges in the operation of Unmanned Aerial Vehicle (UAV) is power optimization under different operation mode. In solving the aforementioned problem, polygamy based selection Genetic Algorithm technique has been proposed in this work. The proposed technique involves parameter initialization, problem coding and optimization. The power requirement is coded as a bit of strings subject...
The subtractive clustering algorithm (SC) is a popular method for data clustering. But the radius of each cluster is an important factor which affects the performances of clustering results. This paper proposes an objective function for the genetic algorithm to estimate the optimal value of this parameter. Two experiments show that the proposed method can automatically obtain this parameter for the...
TSP is a well-known NP-hard problem. Although many algorithms for solving TSP, such as linear programming, dynamic programming, genetic algorithm, anneal algorithm, and ACO algorithm have been proven to be effective, they are not so suitable for the more complicated large scale TSP. This paper offers a method to decompose the large-scale data into several small-scale data sets by its relativity; and...
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