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Research on the Multimedia Data Mining and classification algorithm based on the Database Optimization Techniques

International Journal of Technology Management 2015年第11期 58-60页

作者： Hu Xiu Computer Engineering College Jingchu University of Technology JingmenHubei448000China

In this research article, we analyze the multimedia data mining and classification algorithm based on database optimization techniques. Of high performance application requirements of various kinds are springing up constantly makes parallel computer system structure is valued by more and more common but the corresponding software system development lags far behind the development of the hardware system, it is more obvious in the field of database technology application. Multimedia mining is different from the low level of computer multimedia processing technology and the former focuses on the extracted from huge multimedia collection mode which focused on specific features of understanding or extraction from a single multimedia objects. Our research provides new paradigm for the methodology which will be meaningful and necessary.

关键词： Data Mining classification algorithm Database Optimization Multimedia Source.

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A classification algorithm based on dynamic ensemble selection to predict mutational patterns of the envelope protein in HIV-infected patients

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algorithmS FOR MOLECULAR BIOLOGY 2023年第1期18卷 4-4页

作者： Fili, Mohammad Hu, Guiping Han, Changze Kort, Alexa Trettin, John Haim, Hillel Iowa State Univ Dept Ind & Mfg Syst Engn 3014 Black Engn2529 Union Dr Ames IA 50011 USA Univ Iowa Carver Coll Med Dept Microbiol & Immunol 51 Newton Rd3-770 BSB Iowa City IA 52242 USA

BackgroundTherapeutics against the envelope (Env) proteins of human immunodeficiency virus type 1 (HIV-1) effectively reduce viral loads in patients. However, due to mutations, new therapy-resistant Env variants frequently emerge. The sites of mutations on Env that appear in each patient are considered random and unpredictable. Here we developed an algorithm to estimate for each patient the mutational state of each position based on the mutational state of adjacent positions on the three-dimensional structure of the *** developed a dynamic ensemble selection algorithm designated k-best classifiers. It identifies the best classifiers within the neighborhood of a new observation and applies them to predict the variability state of each observation. To evaluate the algorithm, we applied amino acid sequences of Envs from 300 HIV-1-infected individuals (at least six sequences per patient). For each patient, amino acid variability values at all Env positions were mapped onto the three-dimensional structure of the protein. Then, the variability state of each position was estimated by the variability at adjacent positions of the *** proposed algorithm showed higher performance than the base learner and a panel of classification algorithms. The mutational state of positions in the high-mannose patch and CD4-binding site of Env, which are targeted by multiple therapeutics, was predicted well. Importantly, the algorithm outperformed other classification techniques for predicting the variability state at multi-position footprints of therapeutics on *** proposed algorithm applies a dynamic classifier-scoring approach that increases its performance relative to other classification methods. Better understanding of the spatiotemporal patterns of variability across Env may lead to new treatment strategies that are tailored to the unique mutational patterns of each patient. More generally, we propose the algorithm as a new high-performance

关键词： classification algorithm Dynamic ensemble selection HIV-1 Virus evolution K-best classifiers Protein structure

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A classification algorithm for Ground Moving Targets Based on Magnetic Sensors

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Defence Technology（防务技术） 2011年第1期7卷 52-58页

作者：崔逊学刘綦刘坤 Military Network Engineering Department Artillery Academy of PLA Intelligence Information Department Artillery Command Academy of PLA

A novel classification algorithm based on abnormal magnetic signals is proposed for ground moving targets which are made of ferromagnetic material. According to the effect of diverse targets on earth's magnetism,the moving targets are detected by a magnetic sensor and classified with a simple computation method. The detection sensor is used for collecting a disturbance signal of earth magnetic field from an undetermined target. An optimum category match pattern of target signature is tested by training some statistical samples and designing a classification machine. Three ordinary targets are researched in the paper. The experimental results show that the algorithm has a low computation cost and a better sorting accuracy. This classification method can be applied to ground reconnaissance and target intrusion detection.

关键词： information processing magnetic sensor abnormal magnetic signal target detection target classification classification algorithm

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Distributed texture-based land cover classification algorithm using hidden Markov model for multispectral data

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SURVEY REVIEW 2016年第351期48卷 430-437页

作者： Jenicka, S. Suruliandi, A. Einstein Coll Engn Comp Sci & Engn Tirunelveli Tamil Nadu India Manonmaniam Sundaranar Univ Dept Informat Technol Tirunelveli 627012 Tamil Nadu India

Land cover classification is a vital application area in the satellite image processing domain. Texture is a useful feature in land cover classification. In this paper, we propose a distributed texture-based land cover classification algorithm using Hidden Markov Model (HMM). Here, HMM is used for texture-based classification of remotely sensed images. Furthermore, to enhance the performance, data-intensive remotely sensed image is segmented and distributed into parallel sessions. Experiments were conducted on IRS P6 LISS-IV data, and the results were evaluated based on the confusion matrix, classification accuracy, and Kappa statistics. These results indicate that the proposed algorithm achieves a classification accuracy of 88.75%.

关键词： Cartography classification algorithm classification accuracy Distributed algorithms Hidden Markov models Image segmentation Image texture analysis

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Research on Hotel Customer Relationship Management System Based on the classification algorithm

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INTERNATIONAL JOURNAL OF INFORMATION SYSTEMS AND SUPPLY CHAIN MANAGEMENT 2019年第2期12卷 68-75页

作者： Zhao Weili Changchun Vocat Inst Technol Changchun Jilin Peoples R China

The hotel management relationship is a good business strategy for hotels, which can promote the development of a hotel, when a classification algorithm is applied to customer relationship management system. First, the classification algorithm is based on a support vector machine is studied, the nearest neighbor sample density is used, and the corresponding mathematical model is constructed. Second, the procedure of a classification algorithm based on fuzzy support vector machine is designed. Third, a customer acquisition plan based on a classification algorithm is analyzed. Finally, a hotel is used as the research object, and a customer acquisition analysis is carried out, and the results show that the new method has quicker training speed and higher classification correctness.

关键词： classification algorithm Customer Relationship Management Fuzzy Support Vector Machine Hotel

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Parameter selection algorithm of DBSCAN based on K-means two classification algorithm

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JOURNAL OF ENGINEERING-JOE 2019年第23期2019卷 8676-8679页

作者： Chen, Shouhong Liu, Xinyu Ma, Jun Zhao, Shuang Hou, Xingna Guilin Univ Elect Technol Guilin 541000 Peoples R China Jiangsu Univ Zhenjiang 212013 Jiangsu Peoples R China

Clustering algorithm is one of the most important algorithms in unsupervised learning. For density-based spatial clustering of applications with noise (DBSCAN) density clustering algorithm, the selection of neighborhood radius and minimum number is the key to get the best clustering results. Aiming at the problems of traditional DBSCAN algorithm, such as the neighborhood radius and the minimum number of points, this article puts forward two classifications based on K-means algorithm, and gets two clustering centers. Where calculated between two data points and the cluster center-to -center distance, clustering, distance, statistics in a distance of data points within the scope of the search, the number of data points corresponding to the maximum distance value, and thus the parameters for the DBSCAN algorithm to estimate and selection of initial radius of neighborhood with the minimum number of clustering start critical value. When the parameters are iterated and optimized continuously, the data are divided into clusters, and the most suitable neighborhood radius and the minimum point number are obtained. The experimental data analysis show that the improved algorithm reduces the human factors in the traditional algorithm and improves the efficiency, so as to get the accurate clustering results.

关键词： data analysis unsupervised learning pattern clustering pattern classification human factors start critical value clustering DBSCAN algorithm neighbourhood radius experimental data analysis show minimum point number maximum distance value cluster centre-to-centre distance data points clustering centres noise density clustering algorithm unsupervised learning classification algorithm parameter selection algorithm

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Development of classification algorithm for Epileptic Seizures Using Electrocardiogram Signal

Development of Classification Algorithm for Epileptic Seizur...

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IEEE Annual India Conference

作者： Nikita S. Valke B. R. Karthikevan Department of Electronic and Communication Engineering M. S. Ramaiah University of Applied Sciences Bengaluru India

ISBN: (纸本)9781509036479

An epilepsy classification system using electrocardiogram (ECG) data will ease the process of diagnosis. In epileptic patients, the seizures affect Heart Rate Variability (HRV). This emphasizes the importance of autonomic function changes in diagnosing epilepsy. The present work proposes an algorithm that classifies a person as epileptic or nonepileptic using ECG signal. Time Domain Features (TDF) and Frequency Domain Features (FDF), derived from the R-R Intervals (RRI) of ECG signal are utilized. In addition, Statistical Features (SF) are derived from extracted TDF and FDF. The Support Vector Machines (SVM) classifier is used to classify the ECG signal as epileptic or nonepileptic based on the extracted TDF, FDF and SF. The classification accuracy of the proposed method exhibits 97.5%. Analysis on clinical data shows that the proposed combination of TDF, FDF and statistical HRV features gives excellent classification accuracy. These results indicate that the proposed method can be applied to wearable heart rate measuring devices for diagnostic purpose.

关键词： biomedical signal processing classification algorithm epilepsy feature extraction

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Superimposed Rule-Based classification algorithm(SRBCA) for One-Class Multivariate Conditional Anomaly Detection

Superimposed Rule-Based Classification Algorithm(SRBCA) for ...

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作者： Ivy Kim D.Machica Bobby D.Gerardo Ruji P.Medina West Visayas State University

Traditional anomaly detection causes a problem of detecting too numerous false positives in many problem *** this work,a Superimpose Rule-Based classification algorithm(SRBCA) is proposed for conditional anomaly *** algorithm is an enhancement of the traditional OneR *** traditional OneR can generate a set of rules from its attributes with multiple classes,compute the error rate and apply the rule to the attribute with the smallest ***,OneR has a disadvantage for one-class datasets which contains values belonging to the normal *** enhanced algorithm,SRBCA,does not embody very complex rules similar to its ***,SRBCA includes the generation and application of rules from the one-class dataset in an n-dimensional space using *** method was used to evaluate the performance of the classifiers' accuracy which involved training multiple subsets' behavioral and indicator attributes,superimposing rules and testing by using balanced and unbalanced class data to detect and label conditional anomaly data *** paper shows the comparison between SRBCA,One-Class Support Vector Machine(OCSVM) and other anomaly detection classification algorithms for conditional anomaly *** proves that the new method can handle one-class multivariate for conditional anomaly detection with better accuracy.

关键词： one-class classification conditional anomaly detection classification algorithm

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Research on Chinese Text classification algorithm based on Convolutional Neural Network

Research on Chinese Text Classification Algorithm based on C...

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作者： Junchao Wei Xi’an Traffic Engineering Institute

Various information in the era of Internet big data has shown an "explosive" growth,and mining useful information from text data information is one of natural language processing *** addition to major breakthroughs in image recognition,deep learning convolutional neural networks can also be applied to text *** Chinese data as the research object,a new text classification model is constructed by using the CNN algorithm and the jump-gram combination of convolutional neural *** the same time,the traditional Pinyin classification methods are *** simulation experiments,it is proved that the CNN algorithm has a good effect on text classification,and its classification accuracy is as high as 88%.

关键词： classification algorithm Chinese Text Convolutional Neural Network

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A classification algorithm: Data Mining and Mathematical Model

A Classification Algorithm: Data Mining and Mathematical Mod...

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作者： R Cheng X Kong M Yu N Wang Beijing Polytechnic

In this paper, we propose a classification algorithm based on Recency-FrequencyMonetary(RFM) model and K-means data mining method. In addition, the designed algorithm is verified by the experiments on the member data in a large shopping mall. The experiments results show that the proposed algorithm can provide an accurate classification of the ***, some marketing strategies for different classes of members are given according to the classification results.

关键词： classification algorithm RFM model K-means data mining method

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