Classification is one of the most popular behavior prediction tools in behavior informatics (behavior computing) to predict group membership for data instances. It has been greatly used to support customer relationshi...
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Classification is one of the most popular behavior prediction tools in behavior informatics (behavior computing) to predict group membership for data instances. It has been greatly used to support customer relationship management (CRM) such as customer identification, one-to-one marketing, fraud detection, and lifetime value analysis. Although previous studies showed themselves efficient and accurate in certain CRM classification applications, most of them took demographic, RFM-type, or activity attributes as classification criteria and seldom took temporal relationship among these attributes into account. To bridge this gap, this study takes customer temporal behavior data, called time-interval sequences, as classification criteria and develops a two-stage classification framework. In the first stage, time-interval sequential patterns are discovered from customer temporal databases. Then, a time-interval sequence classifier optimized by the particle swam optimization (PSO) algorithm is developed to achieve high classification accuracy in the second stage. The experiment results indicate the proposed time-interval sequence classification framework is efficient and accurate to predict the class label of new customer temporal data.
Big data poses great challenges for social network analysts in both the data volume and the latent dimensions hidden in the unstructured data. In this paper, we propose a comprehensive knowledge extraction approach fo...
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ISBN:
(纸本)9781479912926;9781479912933
Big data poses great challenges for social network analysts in both the data volume and the latent dimensions hidden in the unstructured data. In this paper, we propose a comprehensive knowledge extraction approach for social networks to guide latent dimensions analysis. An improved hypergraph model of social behaviors was then proposed for conveniently conducting multi-faceted analytics in relationships inherent to social media. A real life case study based on Twitter's data was also presented to illustrate the multi-dimensional relations between users based on the categories they co-join and the tweets they co-spread with three orthogonal dimensions of affect analyzed simultaneously, i.e. valence, activation, and intention.
Discovering temporal patterns and changes in tobacco use has important practical implications in tobacco control. This paper presents one of the first comprehensive international studies of seasonal smoking patterns b...
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ISBN:
(纸本)9781467362139;9781467362146
Discovering temporal patterns and changes in tobacco use has important practical implications in tobacco control. This paper presents one of the first comprehensive international studies of seasonal smoking patterns based on online searches performed. Using periodogram and crosscorrelation, we find that smoking-related search behavior shows strong seasonality effect across countries. In addition, there are significant pairwise associations between such seasonality in different countries.
Discovering temporal patterns and changes in tobacco use has important practical implications in tobacco control. This paper presents one of the first comprehensive international studies of seasonal smoking patterns b...
详细信息
ISBN:
(纸本)9781467362146
Discovering temporal patterns and changes in tobacco use has important practical implications in tobacco control. This paper presents one of the first comprehensive international studies of seasonal smoking patterns based on online searches performed. Using periodogram and cross-correlation, we find that smoking-related search behavior shows strong seasonality effect across countries. In addition, there are significant pairwise associations between such seasonality in different countries.
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