Abstract
In the context of B2B (Business To Business) transactions, identifying the potential customers or business partners is essential. Due to the availability of massive amount of data in the Internet, we can now use sophisticated data mining methods to automate the task of discovering potential customers much faster and more effective than ever before. It is important for ASEAN entrepreneurs to take advantage of this new technology. One approach is to analyse geographical distributions of patent firms in US and analyse their trading patterns in order to identify potential business partners for oversea patent firms. In this paper, we propose a method of analysing the geospatial patterns of patent business relations. In particular, we propose a method of finding good quality clusters of patent firms who are actively dealing with other countries. A comparative study of clustering algorithms has been done to find the best clustering algorithm that is suitable for geospatial analysis of business relations.
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References
Bação FL (2006) Geospatial data mining. ISEGI, New University of Lisbon, Lisbon
Bação F, Lobo V, Painho M (2005) Self-organizing maps as substitutes for k-means clustering. In: Computational science–ICCS 2005. Springer
Brezonik PL, Kloiber S, Olmanson L, Bauer M (2002) Satellite and GIS tools to assess lake quality, Citeseer
Celebi ME, Kingravi HA, Vela PA (2013) A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst Appl 40:200–210
Chandrasekaran S, Song I (2016) Sustainability of big data servers under rapid changes of technology. In: Information science and applications (ICISA) 2016. Springer
Cohen LE, Felson M (1979) Social change and crime rate trends: a routine activity approach. Am Sociol Rev 588–608
Cover TM (1968) Estimation by the nearest neighbor rule. IEEE Trans Inf Theory 14:50–55
Dienes L (2002) Reflections on a geographic dichotomy: Archipelago Russia. Eurasian Geogr Econ 43:443–458
Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17:37
Finney MA (2005) The challenge of quantitative risk analysis for wildland fire. For Ecol Manage 211:97–108
Fowler E (1997) Exploring geographic information systems. Cartographic Perspect 32–35
Goodchild M, Haining R, Wise S (1992) Integrating GIS and spatial data analysis: problems and possibilities. Int J Geogr Inf Syst 6:407–423
Grubesic TH, Murray AT (2001) Detecting hot spots using cluster analysis and GIS. In: Proceedings from the fifth annual international crime mapping research conference, 2001
Guha S, Rastogi R, Shim K (1998) CURE: an efficient clustering algorithm for large databases. In: ACM SIGMOD Record, 1998. ACM, pp 73–84
Guha S, Rastogi R, Shim K (1999) ROCK: a robust clustering algorithm for categorical attributes. In: Proceedings of the 15th international conference on data engineering, 1999. IEEE, pp 512–521
Guha S, Meyerson A, Mishra N, Motwani R, O’Callaghan L (2003) Clustering data streams: theory and practice. IEEE Trans Knowl Data Eng 15:515–528
Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31:181–216
Hamfelt A, Karlsson M, Thierfelder T, Valkovsky V (2011) Beyond k-means: clusters identification for GIS. In: Information fusion and geographic information systems. Springer
Hendrickson B, Leland R (1995) An improved spectral graph partitioning algorithm for mapping parallel computations. SIAM J Sci Comput 16:452–469
Huang Z (1997) Clustering large data sets with mixed numeric and categorical values. In: Proceedings of the 1st Pacific-Asia conference on knowledge discovery and data mining (PAKDD), 1997, Singapore, pp 21–34
Iwata T (2003) Precision geolocation determination and pointing management for the advanced land observing satellite (ALOS). Int Geosci Remote Sens Symp III:1845–1848
Karimipour F, Delavar MR, Kinaie M (2005) Water quality management using GIS data mining. J Environ Inf 5:61–71
Karypis G, Han E-H, Kumar V (1999) Chameleon: hierarchical clustering using dynamic modeling. Computer 32:68–75
Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis. John Wiley & Sons
Kimball R, Ross M (2011) The data warehouse toolkit: the complete guide to dimensional modeling. John Wiley & Sons
Lin S (1965) Computer solutions of the traveling salesman problem. Bell Syst Tech J 44:2245–2269
Miller HJ, Han J (2009) Geographic data mining and knowledge discovery. CRC Press
Moon J-Y, Jung H-J, Moon MH, Chung BC, Choi MH (2009) Heat-map visualization of gas chromatography-mass spectrometry based quantitative signatures on steroid metabolism. J Am Soc Mass Spectrom 20:1626–1637
Muleta MK, Nicklow JW (2005) Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model. J Hydrol 306:127–145
Nath SV (2006) Crime pattern detection using data mining. In: 2006 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology workshops, 2006. WI-IAT 2006 Workshops, 2006. IEEE, pp 41–44
Ng RT, Han J (1994) E cient and E ective clustering methods for spatial data mining. In: Proceedings of, 1994, pp 144–155
Ng RT, Han J (2002) Clarans: a method for clustering objects for spatial data mining. IEEE Trans Knowl Data Eng 14:1003–1016
Ornstein AC (2015) The search for talent. Society 52:142–149
Pham D-T, Suarez-Alvarez MM, Prostov YI (2011) Random search with k-prototypes algorithm for clustering mixed datasets. In: Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 2011. The Royal Society, pp 2387–2403
Pham D, Otri S, Afify A, Mahmuddin M, Al-Jabbouli H (2007) Data clustering using the bees algorithm. In: Proceedings of 40th CIRP international manufacturing systems seminar, 2007
Pradhan B, Lee S, Buchroithner MF (2009) Use of geospatial data and fuzzy algebraic operators to landslide-hazard mapping. Appl Geomatics 1:3–15
Sarfraz MS, Tripathi NK, Kitamoto A (2013) Near real-time characterisation of urban environments: a holistic approach for monitoring dengue fever risk areas. Int J Digit Earth 1–19
Sheikholeslami G, Chatterjee S, Zhang A (1998) Wavecluster: a multi-resolution clustering approach for very large spatial databases. In: VLDB, pp 428–439
Shekhar S, Zhang P, Huang Y, Vatsavai RR (2003) Trends in spatial data mining. Next generation challenges and future directions, Data mining, pp 357–380
Song I (2015a) Diagnosis of pneumonia from sounds collected using low cost cell phones. In: 2015 international joint conference on Neural Networks (IJCNN). IEEE, pp 1–8
Song I (2015b) Gaussian hamming distance. In: Neural information processing. Springer, pp 233–240
Song I, Marsh NV (2012) Anonymous indexing of health conditions for a similarity measure. Information Technology in Biomedicine, IEEE Transactions on 16:737–744
Song I, Vong J (2013a) Affective core-banking services for microfinance. In: Computer and information science. Springer
Song I, Vong J (2013b) Assessing general well-being using de-identified features of facial expressions. In: 2013 international conference of soft computing and pattern recognition (SoCPaR). IEEE, 237–242
Song I, Vong J (2013c) Mobile collaborative experiential learning (MCEL): personalized formative assessment. In: 2013 international conference on IT convergence and security (ICITCS). IEEE, pp 1–4
Song I, Lui C, Vong J (2014) Lowering the interest burden for microfinance. Int J Process Manag Benchmarking 4:213–229
Sterlacchini A (2015) Patent oppositions and opposition outcomes: evidence from domestic appliance companies. Eur J Law Econ 1–21
Stim R (2014) Patent, copyright & trademark: an intellectual property desk reference. Nolo
Tam NT, Song I (2016) Big data visualization. In: Information science and applications (ICISA) 2016. Springer
Thompson MP, Calkin DE, Finney MA, Ager AA, Gilbertson-Day JW (2011) Integrated national-scale assessment of wildfire risk to human and ecological values. Stoch Env Res Risk Assess 25:761–780
Velickov S, Solomatine D (2000) Predictive data mining: practical examples. In: 2nd joint workshop on applied AI in civil engineering. Citeseer
Vörösmarty CJ, Douglas EM, Green PA, Revenga C (2005) Geospatial indicators of emerging water stress: an application to Africa. AMBIO: J Hum Environ 34:230–236
Wagstaff K, Cardie C, Rogers S, Schrödl S (2001) Constrained k-means clustering with background knowledge. In: ICML, pp 577–584
Ye X, Wei YD (2005) Geospatial analysis of regional development in China: the case of Zhejiang Province and the Wenzhou model. Eurasian Geogr Econ 46:445–464
Young IT (1977) Proof without prejudice: use of the Kolmogorov-Smirnov test for the analysis of histograms from flow systems and other sources. J Histochem Cytochem 25:935–941
Yu D, Wei YD (2003) Analyzing regional inequality in post-Mao China in a GIS Environment. Eurasian Geogr Econ 44:514–534
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Rana, P., Song, I., Mandal, P., Vong, J. (2017). New Patent Market Analysis Technology for ASEAN Entrepreneurs. In: Mandal, P., Vong, J. (eds) Entrepreneurship in Technology for ASEAN. Managing the Asian Century. Springer, Singapore. https://doi.org/10.1007/978-981-10-2281-4_3
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DOI: https://doi.org/10.1007/978-981-10-2281-4_3
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