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Recent advances in using computer with different fields of sciences produced huge amounts of data. These data represent as an analysis tool and key to overcome many problems. Clustering is a primary process to analyze the data as well as, it's a preprocessing step before other techniques like classification. Density-Based clustering algorithms have advantages like clustering any arbitrary shapes and...
The rapid spread of location-based devices and cheap storage mechanisms, as well as fast development of Internet technology, allowed collection and distribution of huge amounts of user-generated data. These user generated data sometimes are known as georeferenced documents, they have their location information and time of posting embedded with them. These parameters help to retrieve the location information...
In the extraction of halftone anti-counterfeiting information, the image maybe skew, which causes the anti-counterfeiting information cannot be extracted. Using dots characters to construct the synchronous information, we propose a halftone dots detection algorithm based on cluster analysis. This algorithm detects dots with different in the halftone images, then extract the synchronous information...
A novel objective function based clustering algorithm has been introduced by considering linear functional relation between input-output data and geometrical shape of input data. Noisy data points are counted as a separate class and remaining good data points in the data set are considered as good clusters. This noise clustering concept has been taken into the proposed objective function to obtain...
DBSCAN is one of the most common density-based clustering algorithms. While multiple works tried to present an appropriate estimate for needed parameters we propose an alternating optimization algorithm, which finds a locally optimal parameter combination. The algorithm is based on the combination of two hierarchical versions of DBSCAN, which can be generated by fixing one parameter and iterating...
This paper describes the empathy oriented human-robot interaction model. It is projected to design the model capable of different empathic responses (parallel and reactive) during the course of interaction with the user, depending upon the personality and mood factors of the robot. The proposed model encompasses three main stages i.e., Perception, empathic appraisal and empathic expression. Perception...
Clustering is an important algorithm for data mining. FSC is a kind of clustering algorithm based on density, which has been proposed in the journal Science in 2014. FSC only requires one input parameter and has a higher practicability. RFSC, which is an improved algorithm of FSC algorithm, is less sensitive to the input parameters and faster. However, neither RFSC nor FSC can deal with uneven density...
In many machine learning algorithms, a major assumption is that the training samples and the test samples have the same distribution. However, this assumption does not hold in many real applications. In recent years, transfer learning has attracted a significant amount of attention to solve this problem. Among these methods, an effective algorithm based on clustering analysis and re-sampling can correct...
In this paper, a new algorithm for visualization of high-multidimensional data is described. The algorithm follows several steps. At first, centers representing several categories are selected, and Euclidean distances between these centers are calculated in a high-dimensional space. Then these centers are placed in a 2-dimensional space in such a way that distances in this 2-dimensional space are...
The performance of objective function-based fuzzy clustering algorithms depends on the shape and the volume of the clusters, the initialization of the clustering algorithm, the distribution of the data objects, and the number of clusters contained in the data. We propose an extension of Gustafson- Kessel (FGK) fuzzy algorithm by developing adaptive validation criteria for merging of clusters during...
Clustering algorithm can be used for exploring deep information from data base and outlining the characteristics of each class. In this paper, some typical clustering algorithms were reviewed. Then we focused on classifying G-band bright points (GBPs) in high resolution solar photospheric images. It is found that K-means is suitable for detecting the noise points and DBSCAN algorithm is good at extracting...
Data clustering is one of the popular tasks recently used in the educational data mining arena for grouping similar students by several aspects such as study performance, behavior, skill, etc. Many well-known clustering algorithms such as k-means, expectation-maximization, spectral clustering, etc. were employed in the related works for educational data clustering. None of them has taken into consideration...
It is important to distinguish overlapped cell for tracking and segmentation biological cells from images. In this research, a novel or comprehensive method is provided with morphological features of cell and minimum distance for overlapped cells separation (OCS). it’s necessary to say that this algorithm is not based on type and number of Overlapped cells. In this presented method based on a distance...
In this study we analyzed a series of LiDAR point clouds acquired over Taijiang district (part of Fujian province, China). The objective was to detect and extract water surface area from individual LiDAR point cloud, in a parallel means. To this end, interactive visualization of fine-grained data, global cluster algorithms, and statistical investigation were applied. We first rasterized point clouds...
Recently, fiber clustering algorithms have become an important tool in neuroscience for grouping the white matter tracts into anatomically meaningful bundles. The results of clustering can be used for quantification and comparison between different brains to find out abnormalities or unusual features. One essential problem in fiber clustering is to provide a similarity measure for a pair of fibers...
The unsupervised analysis of data-sets, both large in dimension as well as in number of objects, are one of the most challenging tasks in data intense sciences. Especially in astronomy, dedicated survey telescopes generate an enormous amount of complex data. For example the database of the Sloan Digital Sky Survey (SDSS DR10) contains 3 million spectra with ca. 5,000 values each. Analyzing those spectra...
Finding an object in a 3D scene is an important problem in the robotics, especially in assistive systems for visually impaired people. In most systems, the first and most important step is how to detect an object in a complex environment. In this paper, we propose a method for finding an object using geometrical constraints on depth images from a Kinect. The main advantage of the approach is it is...
Virtual Coordinate (VC) based Wireless Sensor Networks (WSNs) are susceptible to attacks resulting from malicious modification of VCs of individual nodes. While the impact of some such attacks is localized, others such as coordinate deflation and wormholes (tunneling) can cause severe disruptions. A comprehensive solution for detection of such attacks is presented that combines Beta Reputation System...
Cluster analysis is a popular technique in statistics and computer science with the objective of grouping similar observations in relatively distinct groups generally known as clusters. In this paper we propose an approach called Manifold Density Peaks Clustering to improve the basic density peaks clustering. It mainly concerns three aspects. First, geodesic distance is adopted to calculate manifold...
This paper proposes a density grid-based algorithm (C_UStream) for clustering on uncertain data stream in sliding window which can find clusters of arbitrary shapes. The statistical summary information of each grid is stored in linked queue structure by using sampling window mechanism. In order to guarantee the validity of clustering, the expired grids in the current window are removed regularly....
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