Enhanced Classification of Service Usages with Human Trajectory Data for Location Recommendation Systems
Suryakumar B1, Ramadevi E2
1Suryakumar B, Ph.D., in Computer Science at Bharathiar University, Coimbatore.
2Ramadevi E , Associate Professor in Computer science at NGM College, Pollachi, India.
Manuscript received on November 17., 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 11788-11795 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9061118419/2019©BEIESP | DOI: 10.35940/ijrte.D9061.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The rapid growth of mobile messaging apps has led to an important process to manage social networks based on the localization of internet traffic in different types of use of in-app services. In the past researches, Improved Multi-Context Trajectory Embedding Model with Service Usage Classification Method (IMC-TEM-SUCM) has been proposed to recommend the locations based on the trajectory data of individuals and their service usage types. In this model, the traffic features were classified by using Random Forest (RF) classifier whereas the outlier was detected by clustering Hidden Markov Model (HMM). However, the RF was supervised classifier which requires knowledge about the class label of data. Also, a huge amount of data was needed to train a clustering HMM. Therefore, in this article, an IMC-TEM with Enhanced SUCM (IMC-TEM-ESUCM) is proposed in which an unsupervised classifier, namely K-means clustering is proposed to classify the service usage types. Initially, traffic flows are split into different sessions and dialogs using a combined hierarchical clustering and thresholding heuristics technique. Then, the traffic features are extracted based on the packet length and time delay. After that, K-means classification is proposed to classify the service usage types and also DBSCAN is proposed to detect the outliers. Finally, the experimental results on two different datasets show that the proposed model achieves higher performance than the existing model in terms of precision, recall, f-measure and accuracy.
Keywords: Location-based Social Networks, Service usage Classification, Random Forest, Clustering-HMM, K-means, DBSCAN .
Scope of the Article: Classification.