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In this paper, we propose a method for representing “neighborhoods” of moving objects such as people. By using neighborhoods, it is possible to determine whether the spatial relations between objects and their surrounding spatial information are strong. It can be said that a neighborhood is the spatial area of an object's attention, interest or concern. It is preferable that neighborhoods move while...
Facial asymmetry is an important characteristic used in a number of applications. It plays a vital role in human perception of attractiveness and as such has been used in psychology including research on facial expressions evaluation as well as in plastic surgery and orthodontics. It has been also recognized as a biometric feature used for identification and has important applications in detection...
Recently, data stream has become popular in many contexts of data mining. Due to the high amount of incoming data, traditional clustering algorithms are not suitable for this family of problems. Many data stream clustering algorithms proposed in recent years considered the scalability of data, but most of them did not attend the following issues: (1) The quality of clustering can be dramatically low...
This paper addresses the density based clustering problem in data mining where clusters are established based on density of regions. The most well-known algorithm proposed in this area is DBSCAN [1] which employs two parameters influencing the shape of resulted clusters. Therefore, one of the major weaknesses of this algorithm is lack of ability to handle clusters in multi-density environments. In...
Many researchers have argued that data mining can improve the performance of intrusion detection system. So as one of important techniques of data mining, clustering is an important means for intrusion detection. Due to the disadvantages of traditional clustering methods for intrusion detection, this paper presents a graph-based intrusion detection algorithm by using outlier detection method that...
Aiming at the irregular and uneven feature of medical time series data, a novel algorithm based on piecewise slope transformation distance for short time series similarity measure is propose. We firstly do some preprocess based on algorithm for key points selected, make the data curve to zigzag shape, then, we measure the distance between two curves based on piecewise slope transformation algorithm...
The ability to control their shape and size and reproducibility of epitaxial quantum dots is considered as key to the performance of many advanced photovoltaic devices. We demonstrated the possibility of extracting real time using reflection high energy electron diffraction (RHEED), the average dot-size, dot-facet orientations and dot densities. Here and within the framework of the synthesis of archetype...
Understanding the mechanisms involved in cell deformation and motility is of major interest in numerous areas of life sciences. Precise quantification of cell shape requires robust shape description tools to be amenable to subsequent analysis and classification. The main difficulty lies in the great variability of cell shapes within so-called ”homogeneous” populations. While basic shape descriptors...
In order to develop a new extending edge method of potential field with reasonable and practical, this paper develops a difference method of extending edge based on the mean value theorem and the maximum value and minimum value theorem. This paper, through designing theoretical model and calculating theoretical data, processes the actual data by difference method of extending edge, analyses the differences...
Automatic coronary extraction has great clinical importance in the effective handling and visualization of large amounts of 3D data. Despite tremendous previous research, coronary extraction remains difficult. Two such difficulties are extraction of both normal and abnormal vessels and reconstruction of exact tree structures based on anatomical knowledge. To solve the first difficulty, we propose...
Abnormal monitoring data contains a wealth of information and is also the concern object of people. For the time series characteristics of monitoring data, using the time series data mining techniques to discover the regularity knowledge from the abnormal sensor monitoring data is feasible method to help the supervisors identify the reason causing the exceptional fluctuation automatically and make...
The statistical Haralick features from the texture description methods GLCM, GLDM, SRDM, NGLCOM, NGLDM and Run-length features from the texture description method GLRLM are widely used to extract features in mammogram images for analysis and classification of abnormality. In this paper a novel feature extraction method based on spectral shape is proposed for classification of abnormality in mammogram...
Efficient extraction of useful knowledge from very large datasets is still a challenge, mainly when the datasets are distributed, heterogeneous and of different quality depending of the various nodes involved. To reduce the overhead cost due to communications, most of the existing distributed clustering approaches generates global models by aggregating local results obtained on each individual node...
Automated splitting of clustered nuclei from images of tissue sections is essential to many biomedical studies. Many existing image segmentation methods tend to produce over-segmented or under-segmented results for clustered nuclei images. In this paper, a new curvature information based image segmentation algorithm is proposed. Through combining curvature information with a distance map, our algorithm...
Trajectory data streams are huge amounts of data pertaining to time and position of moving objects. They are continuously generated by different sources exploiting a wide variety of technologies (e.g., RFID tags, GPS, GSM networks). Mining such amounts of data is challenging, since the possibility to extract useful information from this peculiar kind of data is crucial in many application scenarios...
Use the data mining techniques to discover the regularity knowledge from the gas sensor monitoring history database is very important approach for the supervisors to identify the reason causing the exceptional fluctuation automatically and make the correct decisions promptly. The clustering method based on the DTW distance for the gas time series above the critical level is proposed firstly, thus...
Real-life call detail data (CDR) are used to build a graph of a social network of telecommunication operator customers. Affiliation network is used in graph construction since CDR data are partially kept anonymous. A number of the resulting network properties are examined to prove the correctness of the graph construction algorithm. Cliques in the network and network dynamics are analyzed; suggestions...
Mining of repeated patterns from HTML documents is the key step towards Web-based data mining and knowledge extraction. Many web crawling applications need efficient repeated patterns mining techniques to generate their wrapper automatically. Existing approaches such as tree matching and string matching can detect repeated patterns with high precision, but their performance is still a challenge for...
We consider unsupervised learning of three-dimensional shapes (values) from topographical method. We propose the method based on direct analysis of three dimensional valleys and hills in a topographical region. The first step of this proposed approach is to extract the outer contour of the depression for normalization and description. A dimension reduction approach is then used to examine the three-dimensional...
o reduce the gap between pixel data and the-saurus semantics, this paper presents a novel approach using mapping between two ontologies on images of drop-capitals (also named drop caps or lettrines): In the first ontology, each drop cap image is endowed with semantic information describing its content. It is generated from a database of lettrines images - namely Ornamental Letter Images Data Base...
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