Includes bibliographical references.

Article 1. Collective entity disambiguation based on hierarchical semantic similarity -- Article 2. Design of college english process evaluation system based on data mining technology and internet of things -- Article 3. Image classification of crop diseases and pests based on deep learning and fuzzy systems -- Article 4. Data mining in programs: clustering programs based on structure metrics and execution values -- Article 5. Research on data mining and investments recommendation of individual users based on financial time series analysis.

[Article Title: Collective Entity Disambiguation Based on Hierarchical Semantic Similarity / Bingjing Jia, Hu Yang, Bin Wu, Ying Xing, p. 1-17]

Abstract: Entity disambiguation involves mapping mentions in texts to the corresponding entities in a given knowledge base. Most previous approaches were based on handcrafted features and failed to capture semantic information over multiple granularities. For accurately disambiguating entities, various information aspects of mentions and entities should be used in. This article proposes a hierarchical semantic similarity model to find important clues related to mentions and entities based on multiple sources of information, such as contexts of the mentions, entity descriptions and categories. This model can effectively measure the semantic matching between mentions and target entities. Global features are also added, including prior popularity and global coherence, to improve the performance. In order to verify the effect of hierarchical semantic similarity model combined with global features, named HSSMGF, experiments were carried out on five publicly available benchmark datasets. Results demonstrate the proposed method is very effective in the case that documents have more mentions.

https://doi.org/10.4018/IJDWM.2020040101

[Article Title: Design of College English Process Evaluation System Based on Data Mining Technology and Internet of Things / Hongli Lou, p. 18-33]

Abstract: This article proposes a new idea for the current situation of procedural evaluation of college English based on Internet of Things. The Internet of Things is used to obtain the intelligent data to enhance the teaching flexibility. The data generated during the process of procedural evaluation is carefully analyzed through data mining to infer whether the teacher's procedural evaluation in English teaching can be satisfied.

https://doi.org/10.4018/IJDWM.2020040102

[Article Title: Image Classification of Crop Diseases and Pests Based on Deep Learning and Fuzzy System / Tongke Fan and Jing Xu, p. 34-47]

Abstract: The automatic classification of crop disease images has important value. The classification algorithm based on manual feature extraction has some problems, such as the need for professional knowledge, is time-consuming and laborious, and has difficulty extracting high-quality features. In this article, the theory of the fuzzy system is discussed. The theory of the fuzzy system is applied to the pretreatment of blurred images. A local blurred image deblurring method based on depth learning is proposed. By training convolutional neural network models with different structures, the image of diseases and insect pests is segmented using normalized segmentation algorithms based on spectral graph theory, and the segmentation knot of leaf diseases is obtained. Finally, the optimal network structure is obtained by comparing the segmentation results with the traditional machine learning algorithm. Experiments show that the segmentation results of pests and diseases obtained by this algorithm have better robustness, generalization, and higher accuracy.

https://doi.org/10.4018/IJDWM.2020040103

[Article Title: Data Mining in Programs: Clustering Programs Based on Structure Metrics and Execution Values / TianTian Wang, KeChao Wang, XiaoHong Su, and Lin Liu, p. 48-63]

Abstract: Software exists in various control systems, such as security-critical systems and so on. Existing program clustering methods are limited in identifying functional equivalent programs with different syntactic representations. To solve this problem, firstly, a clustering method based on structured metric vectors was proposed to quickly identify structurally similar programs from a large number of existing programs. Next, a clustering method based on similar execution value sequences was proposed, to accurately identify the functional equivalent programs with code variations. This approach has been applied in automatic program repair, to identify sample programs from a large pool of template programs. The average purity value is 0.95576 and the average entropy is 0.15497. This means that the clustering partition is consistent with the expected partition.

https://doi.org/10.4018/IJDWM.2020040104

[Article Title: Research on Data Mining and Investment Recommendation of Individual Users Based on Financial Time Series Analysis / Shiya Wang, p. 64-80]

Abstract: With the continuous development of financial information technology, traditional data mining technology cannot effectively deal with large-scale user data sets, nor is it suitable to actively discover various potential rules from a large number of data and predict future trends. Time series are the specific values of statistical indicators on different time scales. Data sequences arranged in chronological order exist in our lives and scientific research. Financial time series is a special kind of time series, which has the commonness of time series, chaos, non-stationary and non-linear characteristics. Financial time series analysis judges the future trend of change through the analysis of historical time series. Through in-depth analysis of massive financial data, mining its potential valuable information, it can be used for individual or financial institutions in various financial activities, such as investment decision-making, market forecasting, risk management, customer requirement analysis provides scientific evidence. https://doi.org/10.4018/IJDWM.2020040105