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Application of Modified optics algorithm in E-Commerce Sites Classification and Evaluation

JOURNAL OF ELECTRONIC COMMERCE IN ORGANIZATIONS 2016年第1期14卷 64-75页

作者： Yu, Zhuoxi Jin, Yujia Parmar, Milan Wang, Limin Jilin Univ Finance & Econ Sch Management Sci & Informat Engn Changchun Jilin Peoples R China Hunan Univ Arts & Sci Changde Peoples R China

In the era of the development in network economy, e-commerce sites' operational efficiency is in relation to the development of enterprises. Thus, how to evaluate e-commerce sites have become a hot topic. Due to the evaluation index of e-commerce sites have the characteristics of high dimension and data inhomogeneity, the new method combines PCA with the improved optics algorithm to classify and evaluate the e-commerce demonstration enterprise websites. Firstly, using PCA to reduce the dimension of high-dimensional data. Secondly, for the limitation of optics algorithm in dealing with sparse points, then using the improved optics algorithm in clustering low-dimensional data to evaluate the effect of e-commerce sites and make suggestions.

关键词： Classification E-Commerce Sites Evaluation optics algorithm PCA

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The Application of the optics algorithm in the Maize Precise Fertilization Decision-Making 9th

The Application of the OPTICS Algorithm in the Maize Precise...

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9th IFIP WG 5.14 International Conference on Computer and Computing Technologies in Agriculture (CCTA)

作者： Wang, Guowei Chen, Yu Li, Jian Hao, Yunpeng Jilin Agr Univ Sch Informat Technol Changchun 130118 Peoples R China Jilin Univ Sch Biol & Agr Engn Changchun 130022 Peoples R China Jilin Prov Res Ctr Changchun 130118 Peoples R China

ISBN: (纸本)9783319483573;9783319483566

With the development of computer science and information technology, data mining technology in the field of agriculture in recent years has become a hot research. Corn planting process, rational fertilization can effectively promote the growth of corn, however, no basis and targeted fertilization may cause shortage of low fertility soil fertilization, high fertility soil fertilization overdose. To solve this problem, In this paper, cluster analysis optics algorithm based on density of soil classification, and press the nutrient balance method to calculate the level of soil fertility for each corresponding amount of fertilizer, farmers can be targeted based on fertilizer fertilization. In the town of Yushu City, Jilin Province by Gongpeng for application, compared with the traditional fertilization, fertilizer input savings of 20.5 %, maize yield of about 10 %, not only to meet the needs of farmers, but also achieve a reduction in fertilizer inputs, increase production purposes.

关键词： Data mining Precision fertilization Fertility classification optics algorithm

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Application of improved optics algorithm in intelligent lighting system 4

Application of improved OPTICS Algorithm in intelligent ligh...

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4th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Cui, Xiaoyun Yuan, Songyang Liu, Yifan Shi, Qingsheng Zhengzhou Branch Henan Tobacco Co Zhengzhou 450018 Peoples R China Henan Univ Technol Coll Elect Engn Zhengzhou 450001 Peoples R China

ISBN: (纸本)9781728146898

Recently, Manual control of lighting system is still the main force in most huge events and situations, which contribute serious waste of electricity. For the tobacco distribution center, there are special lighting management methods, such as setting a uniform time to open and close the lighting equipment in the joint workshop. Although a certain amount of electric energy can be saved by this means, it cannot meet the actual demand. In the essay, according to the actual situation of tobacco distribution center joint workshop, the optics algorithm is improved to conduct cluster analysis on the location data of personnel in the workshop, finding out the illume area where the personnel are relatively densely distributed. Besides, it can also calculate the number of lamps that need to be turned on under the condition of meeting the specified illumination. The experimental results demonstrate that the energy saving effect is particularly obvious when the people are widely distributed in the lighting places.

关键词： Clustering optics algorithm Intelligent lighting

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EAST-NBI experimental data processing method based on improved optics algorithm

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FUSION ENGINEERING AND DESIGN 2021年 172卷 112737-112737页

作者： Wang, Jinxin Liu, Zhimin Zhao, Yuanzhe Xie, Yahong Xie, Yuanlai Chinese Acad Sci Inst Plasma Phys Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China

Generally, the accuracy of the key-values extraction algorithm of experimental data will affect the adjustment of the operating parameters of the experimental device, especially when the NBI (Neutral Beam Injector) ion source breakdown occurs. The instability of the internal plasma of the ion source caused by the mismatch of operating parameters is the main cause of breakdown of the NBI ion source. For the problem of poor accuracy of the existing key-values extraction algorithms for experimental data, this paper proposes an improved algorithm based on Ordering Points To Identify the Clustering Structure (optics). By improving the judgment of the corepoints of the data and the method of searching for core-objects, the time complexity of the algorithm is reduced and the accuracy of the experimental data extraction is improved. The results show that the improved optics algorithm can reach the accuracy of the experimental requirements, meet the data analysis requirements of the EAST-NBI long pulse operation, and can also provide a reference for the failure analysis during the experimental operation.

关键词： Ion source Beam extraction optics algorithm Accuracy Breakdown

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The Application of the optics algorithm in the Maize Precise Fertilization Decision-Making

The Application of the OPTICS Algorithm in the Maize Precise...

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The 9th International Conference on Computer and Computing Technologies in Agriculture (CCTA 2015)

作者： Guowei Wang Yu Chen Jian Li Yunpeng Hao School of Information Technology Jilin Agricultural University School of Biological and Agricultural Engineering Jilin University

ISBN: (纸本)9783319483566

With the development of computer science and information technology, data mining technology in the field of agriculture in recent years has become a hot research. Corn planting process, rational fertilization can effectively promote the growth of corn, however, no basis and targeted fertilization may cause shortage of low fertility soil fertilization, high fertility soil fertilization overdose. To solve this problem, In this paper, cluster analysis optics algorithm based on density of soil classification, and press the nutrient balance method to calculate the level of soil fertility for each corresponding amount offertilizer, farmers can be targeted based on fertilizer fertilization. In the town of Yushu City, Jilin Province by Gongpeng for application, compared with the traditional fertilization, fertilizer input savings of 20.5 %, maize yield of about10 %, not only to meet the needs offarmers, but also achieve a reduction in fertilizer inputs, increase production purposes.

关键词： Data mining Precision fertilization Fertility classification optics algorithm

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An independent central point optics clustering algorithm for semi-supervised outlier detection of continuous glucose measurements

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第PartB期71卷 103196-103196页

作者： Zhao, Yuhang Li, Hongru Yu, Xia Ma, Ning Yang, Tao Zhou, Jian Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Shanghai Jiao Tong Univ Affiliated Peoples Hosp 6 Dept Endocrinol & Metab 600 Yishan Rd Shanghai 200233 Peoples R China

Continuous glucose monitoring (CGM) collects a host of time-series sensor data and is committed to fully automated systems for glucose control as an essential part. Outlier data in CGM measurements caused by faults may seriously affect the computation of insulin infusion rates and endanger the safety of patients. In this paper, a semi-supervised outlier detection method is proposed for anomaly detection of glucose concentration measurements based on a density-based clustering algorithm, named independent central point optics (ICP-optics). An optimization function is designed to improve the clustering performance and solve the problem of outlier detection with the distance measurement and information entropy of weighted time series. The proposed method in the application can be configured automatically with no prior knowledge except only some clean samples. The UVa/Padova simulator and real dataset are used to evaluate the performance of the method. Compared with the present work, the statistical results show that the method is much better in effectiveness and superiority. Furthermore, the proposed method also provides a possible reference significance for outlier detection in other practice applications.

关键词： Outlier detection Semi-supervised learning method optics algorithm Independent central point (ICP) Continuous glucose monitoring (CGM)

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Mining typhoon victim information based on multi-source data fusion using social media data in China: a case study of the 2019 Super Typhoon Lekima

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GEOMATICS NATURAL HAZARDS & RISK 2022年第1期13卷 1087-1105页

作者： Wu, Kejie Wu, Jidong Li, Yue Beijing Normal Univ Key Lab Environm Change & Nat Disaster MOE Beijing Peoples R China Beijing Normal Univ Sch Natl Safety & Emergency Management Beijing Peoples R China Acad Plateau Sci & Sustainabil Xining Peoples R China

Based on situational awareness and information sharing, social media are regarded as a significant data source for disaster emergency management. Many studies have shown that social media can be used for rapid damage assessments during typhoon disasters, while few studies were able to extract victim information through social media. This study aims to determine whether and how we can mine accurate typhoon victim locations and spatial distributions using microblogs from Sina Weibo, one of the largest social media platforms in China, using a case study of the 2019 Super Typhoon Lekima. We first used the latent Dirichlet allocation (LDA) algorithm to classify disaster-related microblogs and exclude irrelevant information. Then, the SnowNLP library was applied to calculate the sentiment score. The negative sentiment contained victim and injury information but was not specific enough. Finally, Euclidean distance and Euclidean distance considering vulnerability were select to identify victim locations 72 hours after Lekima landed using Ordering Point to Identify the Cluster Structure (optics) algorithm. Compared to the real victim locations, the hit rate of the former was 23.5%, while the latter was 31.8%. These results demonstrate that victim information recognition based on multi-source data fusion from Sina Weibo could be an effective new method for assisting disaster emergency response and rescue during typhoons.

关键词： Social media emergency management sentiment analysis multi-source data fusion optics algorithm

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Study on complexity of marine traffic based on traffic intrinsic features and data mining

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2019年第3期19卷 619-633页

作者： Li, Yongpan Liu, Zhengjiang Zheng, Zhongyi Dalian Maritime Univ Sch Nav Dalian 116026 Liaoning Peoples R China

The large scale, high speed and increasing number of vessels along with busy sea routes increase the complexity of marine traffic. It is important for traffic controllers or mariners to understand the traffic situation and pay more attention to high-complexity area. In previous studies, density-based clustering algorithm was often used to discover high-density vessel clusters, so as to evaluate collision risk in waters. However, it can be argued that ship's encounter situation was ignored with those algorithms. This paper focuses on complexity modeling of the two encountering ships and clustering using data mining technology. A complexity model is proposed by employing intrinsic features to reflect pair-wise interactions between ships. A clustering method of ship to ship encountering risk is presented on the basis of complexity by proposing a new distance definition, to quickly calculate the complexity of a large number of ships in an area.

关键词： Automatic identification system density-based clustering ship encounter collision risk optics algorithm

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A comprehensive evaluation of optics, GMM and K-means clustering methodologies for geochemical anomaly detection connected with sample catchment basins

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GEOCHEMISTRY 2024年第2期84卷

作者： Hajihosseinlou, Mahsa Maghsoudi, Abbas Ghezelbash, Reza Amirkabir Univ Technol Dept Min Engn Tehran Iran Univ Tehran Coll Engn Sch Min Engn Tehran Iran

The process of data-driven clustering to uncover geochemical anomalies linked to sample catchment basins (SCBs) includes a comprehensive framework to discern areas exhibiting unique geochemical attributes within a specified study area. The Ordering Points to Identify the Clustering Structure (optics) method can serve as a robust methodology for detecting geochemical anomalies in SCBs. This is attributed to its capacity to effectively manage varying cluster densities, adaptively identify cluster numbers, exhibit resilience to noise, and display minimum sensitivity to parameters. A comparison was conducted in this research between the outcomes of the optics clustering algorithm and two traditional clustering techniques, namely the Gaussian Mixture Model (GMM) and K-means clustering. In the following, the Expectation-Maximization (EM) technique is employed to train the GMM for clustering. Moreover, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) as two validate statistical metrics implemented to ascertain the optimal number of components (clusters) belong to the GMM. It should be noted that the effectiveness of the clustering algorithms was further assessed using the Calinski-Harabasz (CH) index and the success-rate curves. optics, a density-based clustering approach, was confirmed to be more effective than K-means and GMM for identifying MVT Pb-Zn anomalies in Varcheh district, western Iran. Furthermore, the specified anomalies show a geo-spatial correspondence with the geological facts, and it has been observed that strong anomalies are more discoverable in close proximity to MVT Pb-Zn occurrences. This work suggests a novel anomaly detection approach based on optics, which exhibits superior performance and data-modeling efficiency. The main emphasis is on effectively distinguishing geochemical anomalies from sample data originating from populations with uncertain distributions.

关键词： optics algorithm Gaussian Mixture Model Anomaly detection Geochemical clustering Machine learning

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A Method for Identifying Gross Errors in Dam Monitoring Data

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WATER 2024年第7期16卷 978页

作者： Chen, Liqiu Gu, Chongshi Zheng, Sen Wang, Yanbo Hohai Univ Natl Key Lab Water Disaster Prevent Nanjing 210098 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Hohai Univ Natl Engn Res Ctr Water Resources Efficient Utiliz Nanjing 210098 Peoples R China

Real and effective monitoring data are crucial in assessing the structural safety of dams. Gross errors, resulting from manual mismeasurement, instrument failure, or other factors, can significantly impact the evaluation process. It is imperative to eliminate such anomalous data. However, existing methods for detecting gross errors in concrete dam deformation often focus on analyzing a single monitoring effect quantity. This can lead to sudden jumps in values of effect quantity caused by changes in environmental variables being mistakenly identified as gross error. Therefore, a method based on Fuzzy C-Means clustering algorithm (FCM) partitioning and density clustering algorithm (Ordering Points To Identify the Clustering Structure, optics) combined with Local Outlier Factor (LOF) algorithm for gross error identification is proposed. Firstly, the FCM algorithm is used to achieve the division of measurement point areas. Then, the optics and LOF algorithms are jointly utilized to determine the gross errors. Finally, the real gross errors are identified by comparing the time of occurrence of the gross errors at measurement points in the same area. Through the case study, the results indicate that the method can effectively identify spurious, gross errors in the monitoring effect quantity caused by environmental mutations. The accuracy of gross error detection is significantly improved, and the rate of misjudgment of gross errors is reduced.

关键词： dam monitoring data gross errors environmental change FCM algorithm optics algorithm LOF algorithm

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