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Superpixel has been widely applied in hyperspectral image processing as a pre-processing step for over-segmentation. However, most superpixel algorithms are difficult to control the segmentation balance between fragmentation and accuracy. In this paper, we propose a superpixel aggregation model to cluster the over-segmentations. Based on the own importance and interrelationship of superpixels, a two-step...
The Euclid distance based K-means clustering is among the hard classification algorithms. When dealing with deterministic remote sensing data, it is difficult to gain satisfactory classification results using K-means algorithm. The traditional K-means clustering algorithm is faced with several shortcomings such as locally converged optimization, being sensitive to initial clustering centers, etc....
Buildings and adjacent objects in the high spatial resolution images Present the spatial correlation due to the spectral similarity. In addition, the spectral details of building top are completely reflected in images because of resolution levels increased from meter to sub-meter. K-means algorithm is a classical clustering algorithm. Fine spectrum, low signal to noise ratio (SNR) and high spatial...
The classification recognition performance is a hot study in the field of remote sensing image. In this paper, texture feature, shape feature, radiation intensity of remote sensing image information were used to initial terrain classification. Then an improved fuzzy c-means algorithm was applied on classification, and it included optimization of determine clustering center, got the number of clustering...
Solar power penetration has made the site-specific energy ratings an essential necessity for utilities, independent systems operators and regional transmission organizations. Since, it leads to the reliable and efficient energy production with the increased levels of solar power integration. This study concentrates on the partitional clustering analysis of monthly average insolation period data for...
Models based on local operators can't preserve texture information. Nonlocal models can be used for many image processing tasks. A main advantage of nonlocal models over classical PDE-based algorithms is the ability to handle textures and repetitive structures. Some nonlocal models along with their Split Bregman algorithms are proposed for image denoising, image inpainting, and image segmentation...
Nowadays high dimensional data plays an important role in many scientific and research applications. A high dimensional data consists of several features or attributes. These data may contain redundant and irrelevant features. The curse of dimensionality is an important problem in data mining and machine learning. In order to reduce the dimensionality of data and to improve the classifier performance,...
In this paper, we study the problem of performing multi-label classification on networked data, where each instance in the network is assigned with multiple labels and the connections between instances are driven by various casual reasons. Networked data extracted from social media or web pages may not reflect the relationship between users in real life accurately. By mining the links that actually...
The flourishing fame and development of big data in recent years made researchers to have a detailed study. Of the all entire emerging big data research topics, classification of data from big data is identified as a great challenge to address as of our analysis. The Classification is the process of categorizing data for its most effective and efficient use. While analyzing large scale patient records,...
Purpose of this paper is to present a computerized way to evaluate CTG recordings, and more specifically use of feature clustering for the classification process. We used a database which contained 552 records and 20 features. Matlab (version R2012a) was used for the experiments. First we performed a reduction of the number of features used in order to end up only with the most useful ones. That set...
The curse of dimensionality refers to the problem that one faces when analyzing datasets with thousands or hundreds of thousands of attributes. This problem is usually tackled by different feature selection methods which have been shown to effectively reduce computation time, improve prediction performance, and facilitate better understanding of datasets in various application areas. These methods...
Network protocol classification plays an important role in modern network security and fine-grained management architectures. The state-of-the-art network protocol classification methods aim to take the advantages of flow statistical features and machine learning techniques. However the classification performance is severely affected by limited supervised information and unknown network protocols...
The widely known classifier chains method for multi-label classification, which is based on the binary relevance (BR) method, overcomes the disadvantages of BR and achieves higher predictive performance, but still retains important advantages of BR, most importantly low time complexity. Nevertheless, despite its advantages, it is clear that a randomly arranged chain can be poorly ordered. We overcome...
Pattern classification or clustering plays important role in a wide variety of applications in different areas like psychology and other social sciences, biology and medical sciences, pattern recognition and data mining. A lot of algorithms for supervised or unsupervised classification have been developed so far in order to achieve high classification accuracy with lower computational cost. However,...
Data inaccuracy is an important problem in wireless sensor networks, since the accuracy is affected by harsh environments and malicious nodes. The reason for this data inaccuracy is the improper identification of outliers. To detect exact outliers in the wireless sensor networks, we propose the relative correlation based clustering (RCC) technique with high data accuracy and low computational overhead...
For classification of High Dimensional data, feature selection is the most important step for obtaining optimal result with respect to processing power required and time taken. Feature selection is a method by which the most relevant feature is selected from a set of features containing redundant and irrelevant features thereby reducing the load on the classification algorithm. This paper proposes...
This paper proposes a new approach for clustering English text documents, based on finding the pair wise correlation of documents in a given set of text documents. The correlation coefficient for each pair of documents is calculated on the basis of ranks given to the words in the documents. The ranking of the words occurring in a document is computed on the basis of weights of the words calculated...
Gene selection is one of important research issues in analysis of gene expression data classification. Current methods try to reduce genes by means of statistical calculations and have used semantic similarity under gene ontology. In this article a technique has been presented based on which in addition to considering biological relation among genes, redundant genes by means of hierarchical clustering...
Approaches to imbalanced classification problem usually focus on rebalancing the class sizes, neglecting the effect of hidden structure within the majority class. The aim of this paper is to highlight the effect of sub-clusters within the majority class on detecting minority class instances, and handle imbalanced classification by learning the structure in the data. We propose a decomposition based...
Multi-label classification has attracted much attention in recent years due to various applications in real world. There have been many algorithms to deal with this problem. However, there is no algorithm that simultaneously exploits the locality in the instance space and label space which plays an important role in generating a satisfactory model. In this paper we present such an algorithm. It utilizes...
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