MATEC Web Conf.
Volume 154, 2018The 2nd International Conference on Engineering and Technology for Sustainable Development (ICET4SD 2017)
|Number of page(s)||6|
|Section||Engineering and Technology|
|Published online||28 February 2018|
- W. J. Stanton, Prinsip Pemasaran, Erlangga, Jakarta, (2001) [Google Scholar]
- J. Chance, 8 Keys to a Strong Marketing Strategy, Online access (July 28 2017), URL: https://www.businessknowhow.com/marketing/blocks.htm,(2017) [Google Scholar]
- M. S. Kahreh, M. Tive, A. Babania, M. Hesan, Analyzing the applications of customer lifetime value (CLV) based on benefit segmentation for the banking sector. Procedia-Social and Behavioral Sciences, 109, 590-594, (2014) [CrossRef] [Google Scholar]
- P. Kotler, Marketing Management: Analysis, Planning, and Control, 4th edition, Prentice-Hall, Englewood Cliffs, N.J, 195, (1980) [Google Scholar]
- P.N. Tan, M. Steinbach, V, Kumar, Introduction to Data Mining, Pearson Education, MA, (2006) [Google Scholar]
- A.K. Jain, M.N. Murty, P.J. Flynn, Data clustering: a review, ACM Computer Survey, 31 (3), 264-323, (1999) [Google Scholar]
- J. Han, M. Kamber, Data Mining: Concepts and Techniques, 2nd edition, Morgan Kaufmann, (2006) [Google Scholar]
- A.K. Jain, Data clustering: 50 years beyond K-means. 19th International Conference in Pattern Recognition (ICPR), 31 (8), 651-666, (2010) [Google Scholar]
- Z. Cai, W. Gong, C.X. Ling, H. Zhang, A clustering-based differential evolution for global optimization, Applied Soft Computing, 11, 1363-1379, (2010) [CrossRef] [Google Scholar]
- W. Kwedlo, A clustering method combining differential evolution with the K-means algorithm, Pattern Recognition Letters, 32, 1613-1621, (2011) [CrossRef] [Google Scholar]
- Z. Huang, Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorial Values, Data Mining and Knowledge Discovery, 2, 283-304, (1998) [CrossRef] [Google Scholar]
- S. Das, A. Abraham, A. Konar, Automatic Clustering Using an Improved Differential Evolution Algorithm. IEEE Transaction on System, Man, and Cybernetics, 38 (1), (2008) [Google Scholar]
- S. M. S. Hosseini, A. Maleki, M. R. Gholamian, Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty, Expert Systems with Applications, 37(7), 5259-5264, (2010) [Google Scholar]
- T. Hong, E. Kim, Segmenting customers in online stores based on factors that affect the customer’s intention to purchase, Expert Systems with Applications, 39(2), 2127-2131, (2012) [CrossRef] [Google Scholar]
- J. T. Wei, M. C. Lee, H. K. Chen, H. H. Wu, Customer relationship management in the hairdressing industry: An application of data mining techniques Expert Systems with Applications, 40(18), 7513-7518, (2013) [Google Scholar]
- H. K. Han, H. S. Kim, S. Y. Sohn, Sequential association rules for forecasting failure patterns of aircrafts in Korean airforce. Expert systems with applications, 36(2), 1129-1133, (2009) [CrossRef] [Google Scholar]
- J. Hipp, U. Güntzer, G. Nakhaeizadeh, Algorithms for association rule mining—a general survey and comparison, ACM sigkdd explorations newsletter, 2(1), 58-64, (2000) [CrossRef] [Google Scholar]
- Y. Li, P. Ning, X. S. Wang, S. Jajodia, Discovering calendar-based temporal association rules, Data & Knowledge Engineering, 44(2), 193-218, (2003) [CrossRef] [Google Scholar]
- S. O. Abdulsalam, K. S. Adewole, A. G. Akintola, M. A. Hambali, Data Mining in Market Basket Transaction: An Association Rule Mining Approach. International Journal of Applied Information Systems (IJAIS), 7(10), 15-20, (2014) [Google Scholar]
- A. Verma, S. D. Khan, J. Maiti, O. B. Krishna, Identifying patterns of safety related incidents in a steel plant using association rule mining of incident investigation reports, Safety science, 70, 89-98, (2014) [CrossRef] [Google Scholar]
- G. Czibula, Z. Marian, I. G. Czibula, Software defect prediction using relational association rule mining, Information Sciences, 264, 260-278, (2014) [CrossRef] [Google Scholar]
- S. Kumar, D. Toshniwal, Analysing road accident data using association rule mining. In IEEE International Conference on Computing, Communication and Security (ICCCS), 1-6, (2015) [Google Scholar]
- M. Hahsler, B. Grün, K. Hornik, Introduction to arules–mining association rules and frequent item sets. SIGKDD Explor, 2(4), (2007) [Google Scholar]
- A. El-Halees, Mining students data to analyze e-Learning behavior: A Case Study. (2009). [Google Scholar]
- Dannyphoto80, Illustration of the kids holding the tips of the wire for the hanging clothes on a white (royalty free), Online access (August 8th 2017), URL: https://www.dreamstime.com/royalty-free-stock-photos-kids-holding-tips-wire-hanging-clothes-illustration-white-background-image32709718, (2017) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.