International Journal of Advanced Networking and Applications

Tang Zhi-Hang ¹

Affiliations
1 School of Computer and Communication, Hunan Institute of Engineering, Xiangtan 411104, CN

Abstract

Data mining is a process of discovering fascinating designs, new instructions and information from large amount of sales facts in transactional and interpersonal catalogs. The main purpose of this function is to find frequent patterns, associations and relationship between various database using different Algorithms. Association rule mining (ARM) is used to improve decisions making in the applications. ARM became essential in an informationand decision-overloaded world. They changed the way users make decisions, and helped their creators to increase revenue at the same time. Bringing ARM to a broader audience is essential in order to popularize them beyond the limits of scientific research and high technology entrepreneurship. It will be able to expand and apply effective marketing strategies and in disease identification frequent patterns are generated to discover the frequently occur diseases in a definite area. The conclusion in all applications is some kind of association rules (AR) that are useful for efficient decision making.

Keywords

Comparative Study, Association Rule Mining, FP Growth, Decision Making.

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Tang Zhi-Hang ¹

Affiliations
1 School of Computer and Communication, Hunan Institute of Engineering, Xiangtan 411104, CN

Abstract

To aid in the decision-making process, recommender systems use the available data on the items themselves. Personalized recommender systems subsequently use this input data, and convert it to an output in the form of ordered lists or scores of items in which a user might be interested. These lists or scores are the final result the user will be presented with, and their goal is to assist the user in the decision-making process. The application of recommender systems outlined was just a small introduction to the possibilities of the extension. Recommender systems became essential in an information- and decision-overloaded world. They changed the way users make decisions, and helped their creators to increase revenue at the same time.

Keywords

Recommender Systems, Collaborative-Based Systems, Nearest Neighbour.

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Tang Zhi-hang ¹, Li Jun ¹, Zhou Yu-Ying ¹

Affiliations
1 School of Computer and Communication, Human Institute of Engineering Xiangtan 411104, CN

Abstract

With the rapid development of big data technology, many 'sleeping' data can be utilized, but the source of data is the key point. The previous methods of obtaining data can no longer meet the demand. This article uses python web crawler to down jacket of Alibaba International Station. Information (shell material, structure type, fill material, process information, and style information) is crawled and stored in the MongoDB database for data sources for apparel information analysis.

Keywords

Data Mining, Python Web Crawler, Clothing Information Analysis, Down Jacket.

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Tang Zhi-hang ¹, Guo Tao ¹, Li Jun ¹, Wu Shi-qi ¹

Affiliations
1 School of Computer and Communication, Hunan Institute of Engineering Xiangtan 411104, CN

Abstract

Use python web crawler to collect data from Trade website. The collected data is down jacketinformation. The fields are shell material, structure type, filling material, process information and style information. This information can be used for data mining, using clustering algorithms, correlation algorithms, etc. to identify potential value, providing decision-making reference for the management of textile andgarment enterprises, with strong practical value. This paper provides a new idea for the development of textile and garment enterprises. The employees of the company screen, deal with the missing data and standardize thedata, and then conduct data mining. The management of the enterprise makes decisions based on the results of data mining to improve decision-making basis and correctness.

Keywords

K-Means Clustering Algorithm, Decision-Making, Intelligent Recommendation System.

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Guo Tao ¹, Wu Shi-qi ¹, Shi Yi-Cheng ¹, Ma Qian-Qian ¹, Tang Zhi-hang ¹

Affiliations
1 School of Computer and Communication, Hunan Institute of Engineering Xiangtan 411104, CN

Abstract

The text topic analysis is the core element of the comprehensive review of clothing products, which can directly understand the views and consumption trends of consumer groups, taking a brand clothing store in JD.com as the research object, by using Python crawler and HANLP natural language processing technology, seven of the top-selling fashion reviews were classified and analyzed. Word frequency statistics, TF-IDF and other methods were used to quantify the text, this paper uses the visualization techniques such as word cloud graph contrast, pyLDAvis dynamic model and Sankey graph to display customers’ attention points and real shopping needs from various angles. The experimental results show that the visual results of online review research based on the theme model of Lda can clearly show the advantages and disadvantages of customer-centered evaluation and clothing, and provide important reference for merchants to improve decision-making and optimize service.

Keywords

Clothing Review; Natural Language Processing; Topic Mining; Visualization.

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Wu Shiqi ¹, Zhao Xing-yu ², Qiu Fenglin ¹, Tang Zhi-hang ¹

Affiliations
1 School of Computer and Communication, Hunan Institute of Engineering Xiangtan 411104, CN
2 Xiangya Nursing School, Central South University, Changsha 410013, CN

Abstract

This project is the design of word cloud analysis program based on web crawler. Taking Jingdong Mall as the platform, it crawls all comment information of designated products, conducts data cleaning and data analysis on the information obtained from the review and crawler, and generates word cloud map.At the same time, the visual analysis of review data can clearly show the advantages and disadvantages of customer-centered evaluations and commodities, and provide an important reference for consumers to choose commodities and businesses to improve decision-making and optimize services. This project is developed by using Python3 language, using PyChart as the IDE, using the requests library, JSON library, World Cloud library and PyMongo library, using Navicat to connect MongoDB, using PyQT5 library to achieve visual interface, and JavaScript+HTML5+ CSS3 +MySQL+ word cloud + Boozing and Bagging algorithm for data analysis and algorithm optimization. In addition to providing consumers with cost-effective, highly evaluated and highly rated goods, it also provides the sellers with more specific data to improve their own defects.

Keywords

: Jingdong crawler; Natural Language Processing; Data mining; Visualization.

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