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首页> 外文期刊>International Journal of Innovative Computing Information and Control >CLUSTERING MOBILITY PATTERNS IN WIRELESS NETWORKS WITH A SPATIOTEMPORAL SIMILARITY MEASURE
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CLUSTERING MOBILITY PATTERNS IN WIRELESS NETWORKS WITH A SPATIOTEMPORAL SIMILARITY MEASURE

机译：具有时空相似性度量的无线网络中的聚类性

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摘要

Clustering is a technique in data mining whose task is to classify objects into groups. In the recent years, it has been utilized to predict mobility behaviors of users for improving the quality and the management of services in wireless networks. Most of the current solutions focus on extending the traditional k-means approach with the numerical data to the categorical ones. However, such an extension paradigm may result in the loss of semantics of the spatio-temporal mobility patterns of users in the wireless network. Moreover, applying the random choice of initial values (or seeds) of the k-means technique may produce a different local optimum in every run time and thus lead to various partitionings. In this paper, we first propose a model for estimating the similarity among mobility patterns based on the weighted combination of Spatial and Temporal Pattern Similarity measures (STPS) of mobile users in wireless networks. Then we introduce the algorithm of Similarity Mobility Pattern based Clustering (SMPC), which is an alternative extension of the traditional k-means technique. Our approach focuses on using the proposed measure STPS to define a new concept of "cluster center" and to construct two novel procedures: a center updating procedure and a seed initialization procedure. We have conducted experiments with various conditions and parameters to investigate the suitability of the proposed similarity measure STPS and the quality of clusters generated from the algorithm SMPC for mobility patterns in the wireless environment. Experimental results have demonstrated that: (ⅰ) Integrating the spatial and temporal characteristics of mobility patterns in the similarity model improves considerably the clustering quality; (ⅱ) Our seed initialization and center updating procedures achieve the stability and the computational speed better than ones with the traditional random initialization; (ⅲ) Our clustering algorithm SMPC with the proposed combination similarity measure is more effective in computation than the other ones.
机译：聚类是数据挖掘中的一项技术，其任务是将对象分类。近年来，已经利用它来预测用户的移动行为，以改善无线网络中的服务质量和管理。当前大多数解决方案都集中在将传统的k均值方法与数值数据扩展到分类方法。然而，这种扩展范例可能导致无线网络中用户的时空移动性模式的语义丧失。此外，应用k均值技术的初始值（或种子）的随机选择可能会在每个运行时间中产生不同的局部最优值，从而导致各种划分。在本文中，我们首先提出一种模型，该模型基于无线网络中移动用户的时空模式相似性度量（STPS）的加权组合来估计移动性模式之间的相似性。然后，我们介绍了基于相似性移动模式的聚类算法（SMPC），它是对传统k均值技术的另一种扩展。我们的方法侧重于使用建议的措施STPS定义“集群中心”的新概念并构建两个新颖的过程：中心更新过程和种子初始化过程。我们已经进行了各种条件和参数的实验，以研究拟议的相似性度量STPS的适用性以及从SMPC算法生成的集群在无线环境中的质量。实验结果表明：（ⅰ）在相似性模型中整合移动性模式的时空特征可以大大提高聚类质量； （ⅱ）我们的种子初始化和中心更新程序比传统的随机初始化具有更好的稳定性和计算速度； （ⅲ）我们的聚类算法SMPC与所提出的组合相似性测度比其他算法更有效。

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