# Using Data Mining for Mobile Communication Clustering and Characterization

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2 2. Let C(n) be a set of clusters. The number n must be provided by user and represents the number of clusters needed to process these data. 3. Step 1 Initialization: Choose random n observations from entire set O and initialize the cluster members to these observations. 4. Step 2 Assign each observation from O set to the nearest cluster. The distance between a cluster and an observation can be a Euclidian distance, a Manhattan distance or another distance. 5. Step 3 Recomputed the entire cluster set C(n) based on their subset of observation. 6. Step 4 If there are any changes on members from cluster set, go to Step 2 7. Final StepShow the structure of the clusters and classify the observations for each cluster. The differences to the classical algorithm arise from the Step 1 and Step 2. Thus, for a faster convergence, the clusters can be chosen to be uniformly distributed in observation space. It is also common practice to initialize clusters with members that are most representative of the entire set of observations. The Euclidean distance was chosen to run the algorithm. For a better representation, all the data was scaled in the range [0,1]. To do this scaling, for each of the components that compose an observation, we determine the maximum value. For an efficient running of the algorithm, the components that make up observations should not be redundant. It is difficult to identify the data redundancy, because it requires additional processing that can result in loss of important information. One possible solution to this would be to apply an algorithm to identify important components (e.g. PCA - Principal Component Analysis), but this is not the part of this article. We mention that the main point of the article is finding available relationships between users and that is why we wanted to analyze the initial data (gross - without alteration) with no changes. For this purpose, an observation is made up of all the data collected from the mobile operator. III. EXPERIMENTAL SETUP AND DATA INPUTS The IP multimedia and telecommunication industry generates and stores a high amount of data, therefore a manual analysis is not possible. Call detail records include descriptive information about each call made in a telecommunication network. Data mining algorithms take as source of information Call Detail Records (CDR s) extracted during calls. To uncover usage patterns, the information extracted requires data processing. The CDR reveals information at the level of individual phone calls. In data mining applications the aim is to extract knowledge at the customer level. One solution is to summarize the call detail records associated with a user into a single record that describes the user calling behaviour [2]. The users records (the attributes) are given as inputs to the clustering algorithm. Data sets used for this analysis contain information describing the calling behaviour of a user. Two sets of CDR data were drawn during a period of one year. Collected data was saved in a table that contains the entire year s records. Additionally, other tables contain customer information for each individual month. The first data set contains user records characterized by 24 attributes, while the second one by 20 attributes. The measures chosen to represent the user attributes in our analysis are described below. Below is the list of attributes included in both data sets: 1. Number of calls for different services used (voice, SMS, data): describe the frequency of usage of each type of call. Calls are restricted according to the direction of call: we will take into consideration only outgoing calls. The total duration and the total number of calls describe the user s activity level. voice_out_calls number of outgoing voice calls; sms_out_calls number of outgoing SMS calls; data_out_calls number of outgoing data calls; 2. Total Duration of calls: voice_out_duration duration of outgoing voice calls; data_out_size size of -outgoing data calls; 3. Calling Behaviour by Day/Time of the day for different call types: describes the calling behaviour of a user during a workday or weekend for different services used weekend_voice_out_calls number of outgoing voice calls during weekends; weekend_sms_out_calls number of outgoing SMS calls during weekend days; weekend_data_out_calls number of outgoing data calls during weekend days; workdays_voice_out_calls number of outgoing voice calls on working days; workdays_sms_out_calls number of outgoing SMS calls on working days; workdays_data_out_calls number of outgoing data calls on working days; worktime_voice_out_calls duration of outgoing voice calls on working hours (8:00 a.m. 6:00 p.m.); worktime_sms_out_calls (duration of outgoing voice calls on working hours (8:00 a.m. 6:00 p.m.)) worktime_data_out_calls duration of outgoing voice calls on working hours (8:00 a.m. 6:00 p.m.); offtime_voice_out_calls duration of voice calls made outside office hours; offtime_sms_out_calls duration of SMS calls made outside office hours; offtime_data_out_calls duration of data calls made outside office hours;

4 exceptions are observed for almost all users, exceptions that can be correlated with changes in their communication behaviour (e.g. vacancy periods or peak business activities). B. Clusters analysis Next, we analyzed the clusters and the relation between attributes inside clusters (Fig. 2). This analysis allows us to identify similar characteristics of employees in every cluster. Aspects like preferred communication channel (voice, SMS, data), length of communication (duration, size), time of communication (day hours) have been investigated. Clusters voice_out_calls sms_out_calls data_out_calls voice_out_duration data_out_size In Fig. 4 the clusters are plotted by their voice and data attributes. Employees in clusters 1, 4, 6 and 5 do not use or use less data communication plans, compared with the ones in 3 and 2 which are more data oriented. The graphic in Fig. 5 shows the clusters by their messaging and data traffic attributes. Figure 2. Clusters centroids The following graphics plot clusters by various couples of attributes. Each circle represents a cluster and its size represents the number of elements contained by it. In Fig. 3, clusters are presented by their number of voice calls and SMS count. It can be observed a direct relation between number of calls and number of messages for clusters 1, 4, 5 and 2. But employees in cluster 3 prefer SMS communication unlike the employees in cluster 6 which prefer voice communication. Figure 5. SMS traffic vs. initiated data traffic Figure 6. Voice out calls vs. average call duration Figure 3. Voice out calls vs. SMS sends Figure 7. Initiated data traffic vs. traffic size Figure 4. Voice out calls vs. initiated data traffic C. Business analysis In the end we tried to identify relations between employees communication behaviour and their business position or activity. We evaluated the job description of the employees in every cluster. In this analysis we found

5 similar positions of the employees inside clusters. For example in Fig. 2, cluster 2 (red) contains employees in top management positions. Cluster 5 (orange) is formed mainly by people that work in sales. Finally, cluster 1 (cyan) contains the less communicative people, the technical ones. V. CONCLUSIONS In this paper we investigated how data mining algorithms can be used to characterize employers communication patterns and their influence on business related activities. ACKNOWLEDGMENT This work was partially supported by the strategic grant POSDRU/89/1.5/S/57649, Project ID (PERFORM- ERA), co-financed by the European Social Fund Investing in People, within the Sectorial Operational Programme Human Resources Development REFERENCES [1] I. O. Folasade, Computational Intelligence in Data Mining and Prospects in Telecommunication Industry, Journal of Emerging Trends in Engineering and Applied Sciences, vol. 2, no. 4, pp , Aug [2] G. M. Weiss, Data Mining in the Telecommunications Industry, IGI Global Section: Service, [3] J. K. Pal, Usefulness and applications of data mining in extracting information from different perspective, Annals of Library and Information Studies, Vol. 58, pp. 7-16, Mar [4] K. Tulankar, M. Kshirsagar, R. Wajgi, Clustering Telecom Customers using Emergent Self Organizing Maps for Business Profitability, International Journal of Computer Science and Technology, Vol. 3, Iss. 1, pp , Mar

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