Application Research of KNN Algorithm Based on Clustering in Big Data Talent Demand Information Classification

2019 ◽  
Vol 34 (06) ◽  
pp. 2050015
Author(s):  
Qingtao Xiao ◽  
Xin Zhong ◽  
Chenghua Zhong
Keyword(s):  
Text Categorization ◽  
Clustering Algorithm ◽  
Supply And Demand ◽  
Distance Measurement ◽  
Mobile Internet ◽  
Fuzzy Distance ◽  
Bee Colony ◽  
Noise Data ◽  
Demand Information ◽  
Artificial Classification

With the growth of massive data in the current mobile Internet, network recruitment is gradually growing into a new recruitment channel. How to effectively mine available information in the massive network recruitment data has become the technical bottleneck of current education and social supply and demand development. The renewal of talent demand information is carried out every day, which produces a large amount of text data. How to manage these talents’ demand information reasonably becomes more and more important. Artificial classification is time-consuming and laborious, which is unrealistic naturally. Therefore, using automatic text categorization technology to classify and manage this information becomes particularly important. To break through the bottleneck of this technology, a heuristic KNN text categorization algorithm based on ABC (artificial bee colony) is proposed to adjust the weight of features, and the similarity between test observation and training observation is measured by using the method of fuzzy distance measurement. Firstly, the recruitment information is segmented and feature selection and noise data elimination are carried out by using term frequency-inverse document frequency (TF-IDF) algorithm and AP (affinity propagation) clustering algorithm. Finally, the text information is classified by using KNN algorithm combined with heuristic search and fuzzy distance measurement. The experimental results show that this method effectively solves the problem of poor stability and low classification accuracy of traditional KNN algorithm in text categorization method for talent demand.

scholarly journals The priority classification method of mobile users under direct power transaction

2018 ◽  
Vol 242 ◽  
pp. 01013
Author(s):  
Wang Lei ◽  
Gao Chuncheng ◽  
Zhang Qian ◽  
Dai Yong
Keyword(s):  
Electricity Market ◽  
Clustering Algorithm ◽  
Mobile Internet ◽  
Internet Technology ◽  
Classification Method ◽  
Mobile Users ◽  
Direct Power ◽  
Analytic Hierarchy ◽  
Index Set ◽  
Four Dimensions

With the development of mobile internet technology, market-oriented electricity reform forms a new transaction mode that "Internet + power trading", which further promotes the liberalization of retail side market. A large number of mobile power users actively participate in the electricity market transaction. However, the current standards for mobile users who are accessed to the market are qualitative standards, lacking of quantitative analysis. The priority classification method of mobile users under direct power transaction is proposed in the paper. First, the priority evaluation index set of mobile users is established from four dimensions that credit condition, environmental condition, power consumption condition and load importance to achieve evaluation quantitatively. Secondly, Kmeans clustering algorithm based on SOM is used to cluster mobile users. Finally, the paper recognizes the various types of mobile users as a whole and estimates their priorities using the improved AHP(Analytic Hierarchy Process).The method realizes the specific division of mobile users from a point of quantitative view, and solves the problem of only qualitative standard currently, to optimize the development of direct power transaction and promote the marketization of power market.

The fast clustering algorithm for the big data based on K-means

2020 ◽  
Vol 18 (06) ◽  
pp. 2050053
Author(s):  
Ting Xie ◽  
Taiping Zhang
Keyword(s):  
Big Data ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Feature Space ◽  
Data Sets ◽  
Benchmark Data ◽  
Clustering Model ◽  
Alternating Direction ◽  
Learning Technique ◽  
Noise Data

As a powerful unsupervised learning technique, clustering is the fundamental task of big data analysis. However, many traditional clustering algorithms for big data that is a collection of high dimension, sparse and noise data do not perform well both in terms of computational efficiency and clustering accuracy. To alleviate these problems, this paper presents Feature K-means clustering model on the feature space of big data and introduces its fast algorithm based on Alternating Direction Multiplier Method (ADMM). We show the equivalence of the Feature K-means model in the original space and the feature space and prove the convergence of its iterative algorithm. Computationally, we compare the Feature K-means with Spherical K-means and Kernel K-means on several benchmark data sets, including artificial data and four face databases. Experiments show that the proposed approach is comparable to the state-of-the-art algorithm in big data clustering.

scholarly journals Upgrading Development and Application of Agricultural Information Service System Based on WeChat Public Platform— —A Case of WeChat Public Platform of 12396 Beijing new rural sci-tech service hotline

2018 ◽  
Vol 176 ◽  
pp. 03016
Author(s):  
WeiShui Yu ◽  
Qiang Guo ◽  
ChangShou Luo ◽  
YaMing Zheng ◽  
Lu Yang ◽  
...  
Keyword(s):  
Service System ◽  
Supply And Demand ◽  
Information Service ◽  
Agricultural Science ◽  
Business Services ◽  
Consultation Process ◽  
Existing Problems ◽  
Demand Information ◽  
One Stop ◽  
Expert Answer

The WeChat public platform is a new service platform based on WeChat applications that provides business services and user management for individuals, businesses and organizations. It provides a new solution to the lag of agricultural information dissem-ination in the traditional media. However, services based on Tencent's backend can only meet the basic needs of users and reduce the user's experience in agricultural science and technology consulting process. In order to provide efficient and convenient service to users and solve existing problems in consultation process, the platform-related API were used to upgrade the 12396 hotline WeChat public platform to an integrated agricultural information interactive service system. It mainly carries out the following three aspects to upgrade development. Firstly, 12396 hotline WeChat public platform added two sub-menu items, “use ask” and “expert answer” to push the user’s question to expert directly, reducing the backend customer service’s works and improving the efficiency of problem solving. Secondly, 12396 WeChat public platform upgraded and developed an integrated "one-stop" micro-site to allow users know the agriculture market timely, releasing supply and demand information, etc. Thirdly, the 12396 WeChat public platform combined initiative push information with the user's automatic acquisition of information by sending keywords which increased user selectivity. The upgraded platform has obtained a good preliminary application achievement and recognized and welcomed by users. Finally, we concluded and looked forward to the future development direction of agricultural mobile consulting.

Improved Artificial Bee Colony Clustering Algorithm Based on K-Means

2014 ◽  
Vol 556-562 ◽  
pp. 3852-3855 ◽  
Author(s):  
Xue Mei Wang ◽  
Jin Bo Wang
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Bee Colony ◽  
The Poor ◽  
Algorithm Stability ◽  
Clustering Quality ◽  
Local Optimal Solution ◽  
Clustering Center

According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. Artificial Bee Colony algorithm based on K-means was introduced in this article, then put forward an improved Artificial Bee Colony algorithm combined with k-means clustering algorithm at the same time. The experiments showed that the method has solved algorithm stability of k-means clustering algorithm well, and more effectively improved clustering quality and property.

Public Opinion Hotspot Discovery Algorithm Based on Fuzzy Clustering LDA

2013 ◽  
Vol 433-435 ◽  
pp. 626-629
Author(s):  
Hong Xin Wan ◽  
Yun Peng
Keyword(s):  
Public Opinion ◽  
Key Words ◽  
Fuzzy Clustering ◽  
Fuzzy Set ◽  
High Probability ◽  
Clustering Algorithm ◽  
Precision Data ◽  
Topic Extraction ◽  
Noise Data ◽  
Topic Clustering

The discovery of public opinion hotspot is an important aspect of public opinion research, and because many similarities and relevance exist between hot topics, we propose a hot topic clustering algorithm to find the hotspot in public opinions. Since fuzzy set can handle non-precision data well, the fuzzy algorithm can reduce the influences of the uncertainty of public opinion data. Based on LDA topic extraction we cluster the topical words by fuzzy method, and take the topic probability as word membership to the cluster. It can reduce the noise data and improve the ability of hotspot discovery that aggregate the similar and related topic to one class. The topical key words with high probability in cluster are the hotspot, and singular cluster with few words can be looked as outlier. The algorithm is demonstrated by example analysis in detail.

Sign in / Sign up

Export Citation Format

Share Document