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Detection of circular markers in a complex environment is an important task for the unmanned rotorcraft. In this paper, according to the analysis and comparison of computational complexity, noise sensitivity and detection accuracy between the standard Hough transform (SHT) and the gradient Hough transform (GHT) algorithm, it is proposed a hybrid algorithm for circular markers detection (HA-CMD) which...
Linear text segmentation aims at dividing a long text into several topical segments. It is beneficial to many natural language processing tasks, such as information retrieval and document summarization. In this article, an efficient linear text segmentation algorithm based on hierarchical agglomerative clustering is presented. The proposed linear text segmentation algorithm is implemented without...
Data mining has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data". Clustering is the automated search for group of related observations in a data set. The K-Means method is one of the most commonly used clustering techniques for a variety of applications. This paper proposes a method for making the K-Means algorithm...
Identification of post-translational modifications (PTMs) is an important task for understanding biological functions in proteomics. From tandem mass spectra, the identification algorithms attempt to discover accurate PTMs with short time. However, spectral imperfection, such as noise peaks and missing peaks, in tandem mass spectra provokes computational artifacts and often hampers the identification...
Clustering analysis method is one of the main analytical methods in data mining, the method of clustering algorithm will influence the clustering results directly. This paper discusses the standard k-means clustering algorithm and analyzes the shortcomings of standard k-means algorithm, such as the k-means clustering algorithm has to calculate the distance between each data object and all cluster...
Clustering is considered as the most important unsupervised learning problem. It aims to find some structure in a collection of unlabeled data. Dealing with a large quantity of data items can be problematic because of time complexity. On the other hand high dimensional data is a challenge arena in data clustering e.g. time series data. Novel algorithms are needed to be robust, scalable, efficient...
Hierarchical clustering is one of the most important tasks in data mining. However, the existing hierarchical clustering algorithms are time-consuming, and have low clustering quality because of ignoring the constraints. In this paper, a Hierarchical Clustering Algorithm based on K-means with Constraints (HCAKC) is proposed. In HCAKC, in order to improve the clustering efficiency, Improved Silhouette...
In view of ignoring semantic relationship between words, high dimensionality of data and computational complexity when current text clustering algorithms deal with Chinese texts. This paper presents a new method to cluster Chinese texts based on semantics in a specific field-TCBS (Text Clustering Based on Semantics) algorithm. The algorithm is based on the agglomerative hierarchical clustering algorithm,...
The clustering agglomerative hierarchical algorithm for date grouping is considered. To reduce algorithmic complexity without accuracy losses an approach with the speed and accuracy coefficient is proposed. Some results with quality characteristics of clustered data are presented.
There are a large quantity of non-certain and non-structure contents in the Web text at the present time. It is difficult to cluster the text by some normal classification methods. An algorithm of Web text clustering analysis based on fuzzy set is proposed in this paper, and the algorithm has been described in detail by example. The technique can improve the algorithm complexity of time and space,...
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