As association rules widely used, it needs to study many problems, one of which is the generally larger and multi-dimensional datasets, and the rapid growth of the mount of data. Single-processor's memory and CPU ...
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As association rules widely used, it needs to study many problems, one of which is the generally larger and multi-dimensional datasets, and the rapid growth of the mount of data. Single-processor's memory and CPU resources are very limited, which makes the algorithm performance inefficient. Recently the development of network and distributed technology makes cloud computing a reality in the implementation of association rules algorithm. In this paper we describe the improved Apriori algorithm based on MapReduce mode, which can handle massive datasets with a large number of nodes on Hadoop platform.
In order to develop an automatic and rapid detection method for enumeration of total bacteria in juice,biomimetic patternrecognition and machine vision were *** characteristic data,such as shape,texture and color fea...
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In order to develop an automatic and rapid detection method for enumeration of total bacteria in juice,biomimetic patternrecognition and machine vision were *** characteristic data,such as shape,texture and color features,were acquired by using the machine vision technology from bacteria images in varieties of *** on multi-weight higher order neuron network,the recognition models were established which can achieve the imitation of human learning,memorizing and *** applying the principle of statistics,the detection results of new method showed no difference,compared with the traditional method in apple juice,tomato juice and carrot *** new method simplifies experimental preparation and shortens judgment time,especially in sample test on the spot and monitoring production ***,by using this rapid detection method,total bacteria counts in samples could be accurately enumerated within 1 h,which was much less than 24-48 h by using the traditional method.
There has been a rapid convergence to location based services for better resources management. This is made possible by rapid development and lower cost of mobile and handheld devices. Due to this widespread usage how...
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There has been a rapid convergence to location based services for better resources management. This is made possible by rapid development and lower cost of mobile and handheld devices. Due to this widespread usage however, localization and positioning systems, especially indoor, have become increasingly important for resources management. This requires information devices to have context awareness and determination of current location of the users to adequately respond to the need at the time. There have been various approaches to location positioning to further improve mobile user location accuracy. In this work, we examine the location determination techniques by attempting to determine the location of mobile users taking advantage of signal strength (SS) and signal quality (SQ) history data and modeling the locations using extreme learningmachine algorithm (ELM). The empirical results show that the proposed model based on the extreme learning algorithm outperforms k-Nearest Neighbor approaches.
Tongue diagnosis is widely used in the Traditional Chinese Medicine (TCM) and tongue image classification based on patternrecognition plays an important role in the development of the modernization of TCM. However, d...
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Tongue diagnosis is widely used in the Traditional Chinese Medicine (TCM) and tongue image classification based on patternrecognition plays an important role in the development of the modernization of TCM. However, due to labeled tongue samples are rare and costly or time consuming to obtain, most of the existing methods such as SVM utilize labeled training samples merely. Therefore the classifiers usually have poor performance. In contrast, Universum SVM is a promising method which incorporates a priori knowledge into the learning process with labeled data and irrelevant data (also called universum data). In tongue image classification, the number of irrelevant instances could be very large since there are many irrelevant categories for a certain tongue's type. But not all the irrelevant instances joined in training can improve the classifier's performance. So an algorithm of selecting the universum samples is also introduced in this paper. Experimental results show that the Universum SVM classifier is improved and the algorithm of selecting universum samples is effective.
Medical datamining is so *** this paper,we propose a new datamining algorithm called GAJA2,which is a derivation of GAJA [1].We apply GAJA2 to mine Acute Inflammations data set,a medical data set got from UCI machin...
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Medical datamining is so *** this paper,we propose a new datamining algorithm called GAJA2,which is a derivation of GAJA [1].We apply GAJA2 to mine Acute Inflammations data set,a medical data set got from UCI machinelearning repository 2009[2].This data set is about symptoms and diagnosis of two diseases of urinary system which are inflammation of urinary bladder and Nephritis of renal pelvis *** results show that knowledge mined by using GAJA2 is very *** compare the results from GAJA2 with GAJA and Rough Set *** found that the results from GAJA2 can be used by the experts in the fields and are very much easier to understand than from GAJA and Rough Set Theory.
In this paper, a novel fuzzy support vector machine based image watermarking scheme is *** the application of support vector machine in the process of watermarking technology is only a simple classification of the ima...
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In this paper, a novel fuzzy support vector machine based image watermarking scheme is *** the application of support vector machine in the process of watermarking technology is only a simple classification of the image. However,the fuzzy support vector machines by selecting the appropriate degree of membership to reflect the different importance of the different sample points. In this article, Firstly, we split the given image into 8 * 8 block, then calculated for each sub-block of the texture features as input vectors to train the support vector machine. The image sub-block is divided into two categories( one category is “-1” represents a weak texture, the other is “+1” represents a strong texture), and we make the strong texture as more important category in this paper. Therefore, given its larger fuzzy membership than the weak texture's. From the relevant theory of watermarking technology we know that a strong local image texture can tolerate more of the watermark information. In order to enhance watermark robustness and embedding more watermark information in the host image, we improve the accuracy of classifying one class(a strong texture). If the data points belonging to the class of strong texture are incorrectly categorized into the weak texture class, the amount of embedded watermarks are reduced and robustness(the ability of resisting attacks) of the image is decreased. Results show that this algorithm has good robustness against common image attacks.
Subspace learning based face recognition methods have attracted considerable interests in recently years. However, the accuracies of previous methods are not so high, because they don’t utilize the manifold of face i...
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Subspace learning based face recognition methods have attracted considerable interests in recently years. However, the accuracies of previous methods are not so high, because they don’t utilize the manifold of face image data sufficiently and neglect some particular characters of the images. Thus a new method to form graph of data is proposed in this paper, and the method is used to develop two face recognition algorithms. At the same time the pixels correlation in images is considered sufficient under the constrain of spatially smooth in the two developed algorithms. So the features of the projected subspace based on our algorithms have better classification ability. Therefore, the right recognition rates are enhanced by the two proposed algorithms. This is further confirmed by experiments.
Network security is becoming an increasingly important issue, since the rapid development of the Internet. Network Intrusion Detection System (IDS), as the main security defending technique, is widely used against suc...
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Network security is becoming an increasingly important issue, since the rapid development of the Internet. Network Intrusion Detection System (IDS), as the main security defending technique, is widely used against such malicious attacks. datamining and machinelearning technology has been extensively applied in network intrusion detection and prevention systems by discovering user behavior patterns from the network traffic data. Association rules and sequence rules are the main technique of datamining for intrusion detection. Considering the classical Apriori algorithm with bottleneck of frequent itemsets mining, we propose a Length-Decreasing Support to detect intrusion based on datamining, which is an improved Apriori algorithm. Experiment results indicate that the proposed method is efficient.
Educational datamining is a crucial application of machine *** KDD Cup 2010 Challenge is a supervised learning problem on educational data from computer-aided *** task is to learn a model from students' historica...
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Educational datamining is a crucial application of machine *** KDD Cup 2010 Challenge is a supervised learning problem on educational data from computer-aided *** task is to learn a model from students' historical behavior and then predict their future *** paper describes our solution to this *** use different classification algorithms,such as KNN,SVD and logistic regression for all the data to generate different results,and then combine these to obtainthe final *** is shown that our resultsarecomparable to the top-ranked ones in leader board of KDD Cup 2010.
There is a considerable noise in the measured signal of pressure and flow of a running pipeline due to friction drag and medium diffusion, which poses an obstacle to the quick detection and precise classification of p...
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There is a considerable noise in the measured signal of pressure and flow of a running pipeline due to friction drag and medium diffusion, which poses an obstacle to the quick detection and precise classification of pipeline leakage, especially to the acquiring of weak incipient fault. This paper offers an incipient fault detection method based on nonlinear manifold learning algorithm, which treats the negative pressure wave signal as transient signal and reduces noise of original signal by using multi-scale wavelet transform. The method also learns original fault signal and extracts the intrinsic manifold features of data by using a nonlinear dimensionality reduction algorithm based on Laplacian Eigenmaps. With this method, the identification efficiency of optimal fault characteristics is noticeably improved, and the advantage of this method has been proved by simulation experiments.
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