Abstract
Based on the parallel K-means algorithm, this article conducts in-depth research on the related issues of marketing node detection under the Internet, including designing a new Internet marketing node detector and a location summary network based on FCN (Full Convolutional Network) to input the preprocessing of the node and verify its performance under the data sets. At the same time, to solve the problem of insufficient data sets of Internet marketing nodes, the Internet data sets are artificially generated and used for detector training. First, the multiclass K-means algorithm is changed to two categories suitable for Internet marketing node detection: marketing nodes and background categories. Secondly, the weights in the K-means algorithm are mostly only applicable to target detection tasks. Therefore, when processing Internet marketing node detection tasks, the K-means algorithm is used to regress the training set and calculate 5 weights. During the simulation experiment, the weight calculation formula is used to calculate the weight of the feature term. The basic idea is that if a feature word appears more often in this document but less frequently in other nodes, the word will be assigned higher. At the same time, this article focuses on k. Some shortcomings of the mean clustering algorithm have been specifically improved. By standardizing the data participating in the clustering, the data participating in the clustering is transformed from an irregular distribution to a cluster-like distribution, thereby facilitating the clustering process. The density is introduced to determine the initial center of the cluster, and the purity metric is introduced to determine the appropriate density radius of the cluster center, to achieve the most effective reduction of the support vector machine training samples.
Highlights
Introduction e scale of theInternet is increasing day by day, and at the same time, a huge amount of relevant data is generated. e traditional Internet marketing forecasting technology is constrained by the computer’s performance and programming model, resulting in a bottleneck, and it is significantly helpless when processing these data [1]
In the case of massive data and high-dimensional data, a single processor is limited by computing power and memory capacity, so a solution for parallel processing by multiple processors has been proposed [2–5]. e most common idea is to divide a large-scale data set into multiple data subsets that are sufficient for single-machine processing and distribute these subsets to each single-processor node for processing
Due to the variety of clustering algorithms, this article only starts with the K-means clustering algorithm, combines the traditional K-means algorithm with the Canopy algorithm, and parallelizes the above two algorithms according to the MapReduce programming model [9]. e improved algorithm is applied to the Internet cloud computing platform
Summary
With the development of Internet technology, the network has an increasing impact on people’s lives. e information on the network mainly exists in the form of voice, node, and so on. Pan et al [14] proposed the SWT (Stroke Width Transformation) feature applicable to marketing nodes. According to the concept dictionary, the synonym concept was mapped to a singleconcept word, and the dimension of the feature vector was reduced to reduce the amount of calculation It is based on the SWT features proposed by people that are applicable to marketing nodes, as well as the improvement and expansion of the above features. Most of the nodes in the same row have the same brightness and color Based on this feature, Huang et al [17] applied the MSER feature to scene marketing node positioning. Researchers propose SWT (Stroke Width Transformation) features that are applicable to marketing nodes. Internet Marketing Prediction Model Construction Based on Parallel K-Means Algorithm
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