摘要
黔东南旅游景点丰富,去黔东南旅游的游客不断增多,旅游分流压力较大,为了解决这问题,需要对黔东南旅游信息资源进行共享处理和研究。提出了基于子网格的黔东南旅游信息资源稀疏性聚类挖掘算法,实现基于移动互联网的黔东南旅游信息资源共享服务。在网络模型中对旅游信息进行子网格分区,构建了基于3G移动网络的旅游资源信息共享网络系统,系统采用的树形结构设计,对资源信息流进行信号模型构建,实现不同应用条件下的最佳匹配权值。对于每个时间片的旅游信息在受到用户访问时,产生任务请求调度指令,产生资源信息流向量模型,采用Baum-Welch算法对隐马尔科夫链模型进行训练,采用子网格分组特征提取,计算数据的稀疏性协方差矩阵,实现对基于子网格的旅游信息资源稀疏性聚类挖掘。研究结果表明,构建的信息资源挖掘算法,挖掘的效果最佳,信息共享预测误差在2%以内,实现了旅游信息的共享服务,提高黔东南地区的旅游服务质量。
Southeast of Guizhou is rich in tourist attractions, tourists to Southeast of Guizhou tourism increasing, shunt pressure, in order to solve this problem, the need for tourism information resources in Southeast of Guizhou, processing and sharing research. Mining algorithm is proposed based on tourism information resources in Southeast of Guizhou based on Sparse Clustering sub grid, realize the sharing of information resources of Southeast of Guizhou tourism service based on mobile internet, using the system, construct the signal model of resource information flow, to achieve the best matching the right values under different application conditions. For each time slice of the tourist information in the received user access, creating a task request scheduling instruction, vector model of resources and information flow, using the Baum-Welch algorithm is used to train hidden Markov chain model, uses the sub grid feature extraction, calculation of the sparsity of the data covariance matrix, realize to excavate the tourist information resources of Sparse Clustering sub grid based on the. The research results show that the algorithm of mining, construction of information resources, mining the best effect, information sharing prediction error of less than 2%, realize the sharing service of travel information, improve the quality of tourism services in Southeast of Guizhou area.
出处
《科技通报》
北大核心
2015年第2期212-214,共3页
Bulletin of Science and Technology
关键词
移动互联网
黔东南旅游
信息资源
数据挖掘
mobile internet
southeast of Guizhou tourism
information resource
data mining
