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Using the large amount of data collected by mobile operators to evaluate network performance and capacity is a promising approach developed in the recent last years. One of the challenge is to study network accessibility, based on statistical models and analytics. In particular, one aim is to identify when mobile network becomes congested, reducing accessibility performance for users. In this paper,...
The use of technology in education has risen so rapidly that many of the e-learning tools have become a great source for data gathering. In response to the growth, learning analytics are developed to extract the meaning from extensively large datasets and optimize learning opportunities for learners. Understanding users' behaviors is one of the key factors that can help educational institutes improve...
In this paper, we present an efficient and accurate mobile-based mask region grow (MRG) algorithm for natural scene segmentation. The algorithm is divided into three modules: first the captured RGB image is transformed to L*a*b* color space, then a suitable mask is generated and finally MRG is applied. The proposed MRG is best suitable for segmenting multiple foreground objects of single type from...
We propose a novel univariate time series decomposition algorithm to partition temporal sequences into homogeneous segments. Unlike most existing temporal segmentation approaches, which generally build statistical models of temporal observations and then detect change points using inference or hypothesis testing techniques, our algorithm requires no domain knowledge, is insensitive to the choice of...
While great success has been demonstrated in numerous tracking algorithm, some challenging problems still remain such as motion, shape deformation and occlusion. In this paper, the proposed algorithm can work robustly to overcome the occlusion and fast movement in real -- world scenarios. A discriminative model based on the Gaussian superpixel model is constructed to descript the change of the target...
Although numerous clustering algorithms can be found in literature, most of existing algorithms require one or more parameters as input, and their clustering performance usually depends heavily on user-specified parameters. Although some methods have been proposed to determine these parameters automatically, the parameter-tuning problem is still open in general. As a graph-theoretic approach to clustering,...
In this paper we propose a new density clustering algorithm under the assumption that clusters are high density regions in the feature space separated by relatively low density ones. There are two novelties for the proposed algorithm: one is to model any dimensional clustering into one dimensional analysis of density distribution along paths through most compact regions between representative points,...
The brain tumor tissue detection allows to localize a mass of abnormal cells in a slice of Magnetic Resonance (MR). The automatization of this process is useful for post processing of the extracted region of interest like the tumor segmentation. In order to detect this abnormal growth of tissue in an image, this paper presents a novel scheme which uses a two-step procedure; the k-means method and...
Cluster validity index Is used for estimating the quality of partitions to a dataset by clustering algorithms, and finding the optimal number of clusters to be partitioned. In this paper, we propose a new validity index, which is based on a dispersion measure and an overlap measure. The dispersion measure estimates the overall data density of the clusters in the dataset; whereas the overlap measure...
The streaming data scenario has brought about unique challenges with it, like outliers detection, large dimensionality and the issue of scalability being at primary focus. The temporal locality is a quite important while, processing evolving data stream (EDS). The inherit patterns present in the data evolves, and hence, the past clusters are no longer valid to the future and also the initial centroids...
With the development of single-person analysis in computer vision, social group analysis has received growing attention as the next area of research. In particular, group detection has been actively studied as the first step of social analysis. Here, group means an F-formation, that is, a spatial organization of people gathered for conversation. Popular group detection methods are based on coincidences...
In view of today's information available, recent progress in data mining research has lead to the development of various efficient methods for mining interesting patterns in large databases. It plays a vital role in knowledge discovery process by analyzing the huge data from various sources and summarizing it into useful information. It is helpful for analyzing the volumes of data in different domains...
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...
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