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دانلود رایکان مقاله انگلیسی ISI با موضوع تجزیه و تحلیل رفتارهای کاربر با کاوش مجموعه داده های شبکه ای بزرگ

عنوان فارسی مقاله:

تجزیه و تحلیل رفتارهای کاربر با کاوش مجموعه داده های شبکه ای بزرگ

عنوان انگلیسی مقاله:

Analysis of user behaviors by mining large network data sets

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4. User behavior patterns

Based on the algorithm presented in Section 3, in this section, we investigate user behavior patterns and extract salient characteristics that indicate the relationships among users’ consumption capacity, communication time, mobility, and locations
4.1. Communication behavior clustering
In order to study the communication behaviors of users, we firstly divide the one million sample users into 3 levels based on their ARPU (from high to low), then we cluster each level of users by applying the FCM algorithm. The proportion and total ARPU contribution of each level is shown in Fig. 3. We know that the famous 20−80 rule in economics indicates that 20% of customers contribute 80% of profits. However, from Fig. 3 we can see that due to fierce competition, the gap of total revenue between each level is narrowed. This indicates that we cannot underrate any level of users. Applying the FCM algorithm for each level of users, the clustering result is shown in Fig. 4. The subgraphs in Fig. 4 present the results of different ARPU levels, from high to low. For the sake of convenience, we define the clusters as c1, c2 and c3 in a certain ARPU level, respectively. The proportion of c1, c2 and c3 is illustrated on the left of each subgraph. From Fig. 4, we can see that for the top ARPU level, c1 has longer outgoing and incoming calling durations in the morning than afternoon or evening and night. This situation is reversed for c2. The cluster c3 has sharp peak calling durations in the evening around 20:00 h. Generally, the top ARPU level users have longer outgoing call duration than the incoming one, which indicates that they have strong affordability.