R-function f.gupta.R - Examples of PERL and R Program Code

B.6 Examples of PERL and R Program Code

B.6.2 R-function f.gupta.R

# Function which builds a distance matrix from mined

# frequent itemsets according to Gupta/Strehl/Gosh (1999) f.gupta <- function(rulz, trans){

z <- dim(trans)[1]

S <- quality(rulz)[,1]*z

# Transformation of the rules into a matrix (rule matrix) rmx <- as(items(rulz), "matrix")

# The following function adds the j-row to all rows of the rule matrix f.sum <- function(x, j){ x + rmx[j, ] }

for(k in 1:j) {

# Prints the number of each rule for which the function

# is calculating the distance measure print(k)

# Combined item matrix of two rules: for every row of rmx the

# following expression adds with f.sum each j-rule to each

# j-rule of the rule matrix. Since the rows of the resulting

# matrix represent the items, "t()" transposes it and "as()"

# transforms it into an item matrix.

rmxar <- as(t(apply(rmx, 1, f.sum, k)), "itemMatrix")

# Calculating the distance measure between every rule (rows of rmx):

# Due to the presence of item matrix (rmxar), the support of every

# combined rule (scr) can be calculated within the transactions.

for(m in k:i) {

scr <- support(rmxar[m], trans, "absolute")

distm.t[m, k] <- (1 - ( scr / (S[k] + S[m] - scr))) }

}

distm.t <- as.dist(distm.t) return(distm.t)

}

Aaker, D. A., Kumar, V. and George, S. (1998), Marketing Research, Wiley & Sons, New York, NY.

Aamodt, A. and Nyg˚ard, M. (1995), ‘Different roles and mutual dependencies of data, information, and knowledge - an AI perspective on their integration.’, Data Knowl. Eng. 16(3), 191–222.

Acquisti, A. and Varian, H. R. (2005), ‘Conditioning prices on purchase history’, Marketing Science 24(3), 367–381.

Adiguzel, F., Wedel, M. and Zhang, J. (2007), Customizing promotions across multiple categories, PhD thesis, Rijksuniversiteit Groningen. PhD Dissertation: Essays on Customization Applications in Marketing.

Adomavicius, G. and Tuzhilin, A. (2001), ‘Using data mining methods to build customer profiles’, Com-puter 34(2), 74–82.

Agrawal, R., Imielinski, T. and Swami, A. (1993), Mining association rules between sets of items in large databases, in ‘Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data’, Washington, DC, pp. 207–216.

Agrawal, R., Mannila, H., Toivonen, H. and Verkamo, A. I. (1996), Fast discovery of association rules, in U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy, eds, ‘Advances in Knowledge Discovery and Data Mining’, AAAI Press, Menlo Rark, USA, pp. 307–328.

Agrawal, R. and Srikant, R. (1994), Fast algorithms for mining association rules, in ‘Proceedings of the 20th International Conference on Very Large Databases’, Santiago, Chile, pp. 487–499.

Agrawal, R. and Srikant, R. (1995), Mining sequential patterns, in ‘Proceedings of the 11th International Conference on Data Engineering’, Taipei, Taiwan, pp. 3–14.

Ahn, J. Y., Kim, S. K. and Han, K. S. (2003), ‘On the design concepts for CRM system’, Industrial Management and Data Systems 103(5), 324–331.

Ainscough, T. and Aronson, J. (1999), ‘An empirical investigation and comparison of neural networks and regression for scanner data analysis’, Journal of Retailing and Consumer Services 6(13), 205–

217.

Ainslie, A. and Rossi, P. E. (1998), ‘Similarities in choice behavior across product categories’, Marketing Science 17(2), 91–106.

Aldenderfer, M. S. and Blashfield, R. K. (1984), Cluster Analysis, Vol. 44 of Quantitative Applications in the Social Sciences, Sage University Paper, Beverly Hills.

Anderberg, M. R. (1973), Cluster Analysis for Applications, Academic Press, New York.

Anderson, C. (2006), The Long Tail: how endless choice is creating unlimited demand, RH Business Books, London.

Andrews, R. L. and Currim, I. S. (2002), ‘Identifying segments with identical choice behaviors across product categories: An intercategory logit mixture model’, International Journal of Research in Marketing 19(1), 65–79.

Ansari, A., Essagaier, S. and Kohli, R. (2000), ‘Internet recommendation systems’, Marketing Research 37(3), 363–375.

Bacher, J. (1996), Clusteranalyse - Anwendungsorientierte Einf¨uhrung, 2 edn, Oldenbourg Verlag, M ¨unchen, Wien.

Backhaus, K., Plinke, W., Erichson, B. et al. (2006), Multivariate Analysemethoden - Eine anwendung-sorientierte Einf¨uhrung, 11 edn, Springer, Berlin.

Balka, K. (2005), Neue Methoden zur Marktsegmentierung durch Clustern von gruppierten Warenkorb-daten, Master thesis, Technische Universit¨at Wien, Vienna, Austria. Friedrich Leisch, advisor.

Basu, S., Banerjee, A. and Mooney, R. J. (2004), Active semi-supervision for pairwise constrained clus-tering, in ‘In Proceedings of the 2004 SIAM International Conference on Data Mining (SDM-04)’, pp. 333–344.

Basu, S., Davidson, I. and Wagstaff, K. (2008), Constrained Clustering: Advances in Algorithms, Theory, and Applications, Chapman & Hall.

Bayardo, R. J. (1998), Efficiently mining long patterns from databases, in ‘Proceedings of the 1998 ACM-SIGMOD International Conference on Managment of Data’, pp. 85–93.

Bayardo, R. J., Agrawal, R. and Gunopulos, D. (1999), Constraint-based rule mining in large, dense databases, in ‘Proceedings of the 15th International Conference on Data Engineering’, pp. 188–

197.

Bayardo, R. J. and Agrawal, S. (1999), Mining the most interesting rules, in ‘Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining’, pp. 145–154.

Bell, D. R. and Lattin, J. M. (1998), ‘Shopping behavior and consumer preference for store price format:

why “large basket” shoppers prefer EDLP’, Marketing Science 17(1), 66–88.

Berry, M. J. and Linoff, G. S. (2004), Data mining techniques, 2 edn, Wiley, Indianapolis.

Bijmolt, T. H. A., van Heerde, H. J. and Pieters, R. G. M. (2005), ‘New empirical generalizations on the determinants of price elasticity’, JMR, Journal of Marketing Research 42(2), 141–156.

Blattberg, R. C. and Neslin, S. A. (1990), Sales promotion - concepts, methods, and strategies, Prentice Hall, Englewood Cliffs, NJ.

Bock, H.-H. (1999), Clustering and neural network approaches, in W. Gaul and H. Locarek-Junge, eds, ‘Classification in the Information Age, Proceedings of the 22nd Annual Conference of the Gesellschaft f¨ur Klassifikation e.V.’, Springer, Heidelberg, Germany, pp. 42–57.

B ¨ocker, F. (1978), Die Bestimmung der Kaufverbundenheit von Produkten, Vol. 57 of Schriften zum Marketing, Duncker and Humblot.

Bodapati, A. V. (2008), ‘Recommendation systems with purchase data’, Journal of Marketing Research 45(1), 77–93.

Bordemann, H.-G. (1985), Analyse von Verbundbeziehungen zwischen Sortimentsteilen im Einzelhan-del, PhD thesis, Universi¨at Duisburg GH, Duisburg.

Boztug, Y. and Reutterer, T. (2008), ‘A combined approach for segment-specific analysis of market basket data’, EJOR European Journal of Operational Research 187(1), 294–312.

Boztug, Y. and Silberhorn, N. (2006), ‘Modellierungsansätze in der Warenkorbanalyse im Überblick’, Journal für Betriebswirtschaft 56(2), 105–128.

Breimann, L. (2001), ‘Statistical modeling: The two cultures (with comments and a rejoinder by the author)’, Statistical science 16(3), 199–231.

Brijs, T., Goethals, B., Swinnen, G., Vanhoof, K. and Wets, G. (2000), A data mining framework for optimal product selection in retail supermarket data: the generalized PROFSET model, in ‘KDD

’00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining’, ACM Press, New York, NY, USA, pp. 300–304.

Brijs, T., Swinnen, G., Vanhoof, K. and Wets, G. (2004), ‘Building an association rules framework to improve product assortment decisions’, Data Mining and Knowledge Discovery 8(1), 7–23.

Brin, S., Motwani, R. and Silverstein, C. (1997), Beyond market baskets: Generalizing association rules to correlations, in J. Peckham, ed., ‘SIGMOD 1997, Proceedings ACM SIGMOD Interna-tional Conference on Management of Data, May 13-15, 1997, Tucson, Arizona, USA’, ACM Press, pp. 265–276.

Brin, S., Motwani, R., Ullman, J. D. and Tsur, S. (1997), Dynamic itemset counting and implication rules for market basket data, in J. Peckham, ed., ‘SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management of Data, May 13-15, 1997, Tucson, Arizona, USA’, ACM Press, pp. 255–264.

Brusco, M. J. and Cradit, D. J. (2001), ‘A variable-selection heuristic for k-means clustering’, Psychome-trika 66(2), 249–270.

Carmone Jr., F., Kara, A. and Maxwell, S. (1999), ‘A new model to improve market segment definition by identifying noisy variables’, Journal of Marketing Research 36(4), 501–509.

Cavique, L. (2007), ‘A scalable algorithm for the market basket analysis’, Journal of Retailing and Consumer Services 14(6), 400–407.

Chaturvedi, A., Carroll, J. D., Green, P. E. and Rotondo, J. A. (1997), ‘A feature-based approach to market segmentation via overlapping k-centroids clustering’, Journal of Marketing Research 34(3), 370–377.

Cheung, D. W., Ng, V. T., Fu, A. W. and Fu, Y. (1996), ‘Efficient mining of association rules in distributed databases’, IEEE Transactions On Knowledge And Data Engineering 8(6), 911–922.

Chintagunta, P. and Song, I. (2007), ‘A discrete/continuous model for multi-category purchase behavior of households’, Journal of Marketing Research 44(4), 595–612.

Church, A. (1936), ‘A note on Entscheidungsproblem’, Journal of Symbolic Logic 1(1), 40–41. Correc-tion 1 (1936): pp. 101-102.

Cody, W. F., Kreulen, J. T., Krishna, V. and Spangler, W. S. (2002), ‘The integration of business intelli-gence and knowledge management’, IBM Systems Journal 41(4), 697–713.

Cohen, J. (1960), ‘A coefficient of agreement for nominal scales’, Educational and Psychological Mea-surement 20(1), 37–46.

Coombs, C. H. (1964), A Theory of Data, Wiley, New York.

Cormen, T. H. (2007), Algorithmen - Eine Einf¨uhrung, Oldenbourg, M¨unchen.

Cottle, D. (2000), Professional’s guide to target marketing, Harcourt Prof. Publishing.

D’Agostino, R. B. and Stephens, M. A. (1986), Goodness-of-fit techniques, Marcel Dekker Inc., New York.

Dakin, R. J. (1965), ‘A tree-search algorithm for mixed integer programming problems’, The Computer Journal 8(3), 250–255.

Davies, D. L. and Bouldin, D. W. (1979), ‘A cluster separation measure’, IEEE Transactions on Pattern Analysis and Machine Intelligence 1(2), 224–227.

Decker, R. (2005), ‘Market basket analysis by means of a growing neural network’, The International Review of Retail, Distribution and Consumer Research 15(2), 151–169.

Decker, R. and Monien, K. (2003), ‘Market basket analysis with neural gas networks and self-organising maps’, Journal of Targeting, Measurement and Analysis for Marketing 11(4), 373–387.

Decker, R. and Schimmelpfennig, H. (2002), Alternative Ansätze zur datengestützten Verbundmessung im Electronic Retailing, in D. Ahlert, R. Olbricht and H. Schröder, eds, ‘Jahrbuch Handelsmanage-ment 2002’, Frankfurt am Main, pp. 193–212.

Dibb, S. and Simkin, L. (1996), The market segmentation workbook - target marketing for marketing managers, 1. edn, Routledge, London.

Dimitriadou, E., Dolnicar, S. and Weingessel, A. (2002), ‘An examination of indexes for determining the number of clusters in binary data sets’, Psychometrika 67(1), 137–160.

Dimitriadou, E., Weingessel, A. and Hornik, K. (2002), ‘A combination scheme for fuzzy clustering’, International Journal of Pattern Recognition and Artificial Intelligence 16(7), 901–912.

Dolnicar, S., Leisch, F., Steiner, G. and Weingessel, A. (1998), A comparison of several cluster algo-rithms on artificial binary data scenarios from tourism marketing, Working Paper 19, SFB “Adap-tive Information Systems and Modeling in Economics and Management Science”.

URL: http://www.wu-wien.ac.at/am/workpap.htm

Dong, G. and Pei, J. (2007), Sequence Data Mining - Advances in Database Systems, Springer, Berlin.

Dr`eze, X. and Hoch, S. J. (1998), ‘Exploiting the installed base using cross-merchandising and category destination programs’, International Journal of Research in Marketing 15(5), 459–471.

Duchessi, P., Schaninger, C. M. and Nowak, T. (2004), ‘Creating cluster-specific purchase profiles from point-of-sale scanner data and geodemographic clusters: improving category management at a ma-jor us grocery chain’, Journal of Consumer Behaviour 4(2), 97–117.

Duda, R. O., Hart, P. E. and Stork, D. G. (2001), Pattern Classification, 2 edn, Wiley, New York.

Elberse, A. (2008), ‘Should you invest in the long tail?’, Harvard Business Review 86(7/8), 88–96. HBS Centennial Issue.

Fader, P. S., Hardie, B. G. S. and Lee, K. L. (2005), ‘RFM and CLV: Using iso-value curves for customer base analysis’, Journal of Marketing Research 42(4), 415–430.

Fayyad, U., Grinstein, G. G. and Wierse, A. (2002), Information visualization in data mining and knowl-edge discovery, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.

Fayyad, U. M., Piatetsky-Shapiro, G. and Smyth, P. (1996), ‘From data mining to knowledge discovery in databases.’, Artificial Intelligence Magazine 17(3), 37–54.

Fisher, D. H. (1987), ‘Knowledge acquisition via incremental conceptual clustering’, Machine Learning 2, 139–172.

Fox, E. J. and Hoch, S. J. (2005), ‘Cherry-picking’, Journal of Marketing 69(1), 46–62.

Franke, N. and Mazanec, J. (2006), ‘The six scientific identities of marketing: A vector quantization of research approaches’, European Journal of Marketing 5/6(40), 634–661.

Fritzke, B. (1997), Some competitive learning methods.

URL: URL: http://www.neuroinformatik.ruhr-uni-bochum.de/ini/VDM/research/gsn/JavaPaper/

Fukuda, T., Morimoto, Y., Morishita, S. and Tokuyama, T. (1996), Data mining using two-dimensional optimized association rules: Scheme, algorithms, and visualization, in ‘Proc. of the 1996 ACM-SIGMOD International Conference on the Management of Data’, pp. 13–23.

Gonzales, M. L. (2003), IBM Data Warehousing, Wiley.

Gordon, A. D. (1981), Classification, Monographs on Applied Probability and Statistics, Chapman and Hall, London.

Gordon, A. D. and Vichi, M. (1998), ‘Partitions of partitions’, Journal of Classification 15(2), 265–285.

Gosh, A. K. (1997), ‘Targeted promotions using scanner panel data’, Journal of Product and Brand Management 6(6), 405–416.

Gouda, K. and Zaki, M. J. (2005), ‘GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets’, Data Mining and Knowledge Discovery 11(3), 223–242.

Grapentine, T. and Boomgaarden, R. (2003), ‘Maladies of market segmentation - the promise of this technique still remains untapped’, Marketing Research 15(1), 27–31.

Grinstein, G. G. and Ward, M. O. (2002), Introduction to data visualization, in U. Fayyad, G. G. Grinstein and A. Wierse, eds, ‘Information visualization in data mining and knowledge discovery’, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 21–45.

Grünblatt, M. (2001), Verfahren zur Analyse von Scanningdaten - Nutzenpotentiale, praktische Prob-leme und Entwicklungsperspektiven, in R. Olbricht, ed., ‘Berichte aus dem Lehrstuhl für Betrieb-swirtschaftslehre, insb. Marketing’, Vol. 5 of Forschungsbericht, Fernuniversität Hagen - Fachbe-reich Wirtschaftswissenschaften, Hagen.

G ¨unter, T. M. and Klapper, D. (2007), ‘Do the long-run category demand effects of retailer promotions vary across different store types?’, Marketing - Journal of Research and Management 3(1), 17–33.

Gupta, G. K., Strehl, A. and Gosh, J. (1999), Distance based clustering of association rules, in ‘Intelligent Engineering Systems Through Artificial Neural Networks’, Vol. 9, ANNIE, ASME Press, pp. 759–

764.

Hagerup, T. and R ¨ub, C. (1990), ‘A guided tour of chernoff bounds’, Information Processing Letters 33(6), 305–308.

Hahsler, M., Gr¨un, B. and Hornik, K. (2005), ‘A computational environment for mining association rules and frequent item sets’, Journal of Statistical Software 14(15), 1–25.

Hahsler, M., Hornik, K. and Reutterer, T. (2005), Implications of probabilistic data modeling for rule mining, Report 14, Research Report Series, Department of Statistics and Mathematics, Wirtschaft-suniversit¨at Wien, Augasse 2–6, 1090 Wien, Austria.

Hahsler, M., Hornik, K. and Reutterer, T. (2006), Warenkorbanalyse mit hilfe der statistik-software r, in P. Schnedlitz, R. Buber, T. Reutterer, A. Schuh and C. Teller, eds, ‘In Innovationen in Marketing und Handel’, Linde, Wien, pp. 144–163.

Hamming, R. W. (1950), ‘Error detecting and error correcting codes’, Bell System Technical Journal 26(2), 147–160.

Han, J. and Kamber, M. (2000), Data Mining: Concepts and Techniques, Morgan Kaufmann.

Hand, D. J. and Krzanowski, W. J. (2005), ‘Optimising k-means clustering results with standard software packages’, Computational Statistics and Data Analysis 49(4), 969–973.

Harding, F. (2002), Cross selling success, Adams Media, Avon, Mass.

Hartigan, J. A. and Wong, M. A. (1979), ‘A K-means clustering algorithm’, Applied Statistics 28(1), 100–

108.

Hastie, T., Tibshirani, R. and Friedman, J. H. (2001), The Elements of Statistical Learning, Springer, New York, NY.

Heinzl, A. (2001), Zum Aktivitätsniveau empirischer Forschung in der Wirtschaftsinformatik - Erklärung und Handlungsoptionen, in H. B öhler and J. Sigloch, eds, ‘Unternehmensführung und empirische Forschung: Festschrift zum 65. Geburtstag von Peter R ötger Wossidlo’, Arbeitspapier 7/2001, Uni-versiät Bayreuth, Lehrstuhl für Betriebswirtschaftslehre VII - Wirtschaftsinformatik, Bayreuth.

Hettich, S. and Hippner, H. (2001), Assoziationsanalyse, in H. Hippner, U. L. K ¨usters, M. Meyer and K. Wilde, eds, ‘Handbuch Data Mining im Marketing - Knowledge Discovering in Marketing Databases’, Viewag, Wiesbaden, pp. 427–463.

Hilderman, R. J. and Hamilton, H. J. (2001), Knowledge discovery and measures of interest, Kluwer, Boston, Mass.

Homburg, C. (2007), ‘Betriebswirtschaftslehre als empirische Wissenschaft - Bestandsaufnahme und Empfehlung’, ZfbF - Zeitschrift f¨ur betriebswirtschaftliche Forschung 56(7), 27–60.

Hornik, K. (2005a), ‘A CLUE for CLUster Ensembles’, Journal of Statistical Software 14(12), 1–25.

Hornik, K. (2005b), Cluster ensembles, in C. Weihs and W. Gaul, eds, ‘Classification — The Ubiquitous Challenge’, Springer-Verlag, Heidelberg, pp. 65–72. Proceedings of the 28th Annual Conference of the Gesellschaft f¨ur Klassifikation e.V., University of Dortmund, March 9–11, 2004.

Hruschka, H. (1991), ‘Bestimmung der Kaufverbundenheit mit Hilfe eines probalistischen Messmod-ells’, Zeitschrift f¨ur betriebswirtschaftliche Forschung 43(5), 418–434.

Hubert, L. and Arabie, P. (1985), ‘Comparing partitions’, Journal of Classification 2, 193–218.

H ¨ubl, G. and Trifts, V. (2000), ‘Consumer decision making in online shopping environments: The effects of interactive decision aids’, Marketing Science 19(1), 4–21.

Hughes, A. M. (1996), The Complete Database Marketer, McGraw Hill, New York, NY.

Hui, X., Tan, P.-N. and Kumar, V. (2006), ‘Hyperclique pattern discovery’, Data Mining and Knowledge Discovery 13(2), 219–242.

Jäger, A. (2007), RFID als Marketingstrategie : Einsatzpotentiale im Einzelhandel, VDM Verlag M üller, Saarbrücken.

Jamshaid, S., Jalil, Z., Khiyal, M. S. H. and Saeed, M. I. (2007), ‘Association rule mining in centralized databases’, Information Technology Journal 6(2), 174–181.

Jiang, J. J., Klein, G. and Pick, R. A. (1998), ‘A marketing category management system: a decision support system using scanner data’, Decision Support Systems 23(3), 259–271.

Kahn, B. E. and McAllister, L. (1997), Grocery Revolution: The New Focus on the Consumer, Addision-Wesley, Reading, MA.

Kanitscheider, B. (1981), Wissenschaftsthoerie der Naturwissenschaft, de Gruyter.

Kanji, G. K. (1993), 100 Statistical Tests, SAGE Publications, Newbury Park, Cal.

Kaufman, L. and Rousseeuw, P. J. (2005), Finding Groups in Data: An Introduction to Cluster Analysis., John Wiley.

Kelley, S. (1996), Data Warehousing: The Route to Mass Customization, John Wiley & Sons, New York.

Kleinberg, J. M., Papadimitriou, C. H. and Raghavan, P. (1998), ‘A microeconomic view of data mining.’, Data Mining Knowledge Discovering 2(4), 311–324.

Kohonen, T. (1982), ‘Self-organized formation of topologically correct feature maps’, Biological Cyber-netics 43, 59–69.

Kohonen, T. (2000), Self-Organizing Maps, 3 edn, Springer.

Kumara, V. and Shahb, D. (2004), ‘Building and sustaining profitable customer loyalty for the 21st century’, Journal of Retailing 80(4), 317–329.

Kuonen, D. (2005), ‘Is data mining for gold ’statistical d´ej`a vu’?’, CRM Zine 53. Published by CRM Today.

Lal, R. and Bell, D. (2003), ‘The impact of frequent shopper programs in grocery retailing’, Quantitative Marketing and Economics 1(2), 179–202.

Lawrence, R. D., Almasi, G. S., Kotlyar, V., Viveros, M. S. and Duri, S. S. (2001), ‘Personalization of supermarket product recommendations’, Data Mining and Knowledge Discovering 5(1-2), 11–32.

Leenheer, J. (2004), The Adoption and Effectiveness of Loyalty Programs in Retailing, PhD thesis, Tilburg University, Netherlands.

Leisch, F. (2006), ‘A toolbox for k-centroids cluster analysis’, Computational Statistics and Data Anal-ysis 51(2), 526–544.

Leisch, F. and Gr¨un, B. (2006), Extending standard cluster algorithms to allow for group constraints, in A. Rizzi and M. Vichi, eds, ‘Compstat 2006, Proceedings in Computational Statistics’, Physica-Verlag, Heidelberg, pp. 885–892.

Liebermann, Y. (1999), ‘Membership clubs as a tool for enhancing buyers patronage’, Journal of Busi-ness Resarch 45(3), 291–297.

Luo, C. and Chung, S. M. (2004), A scalable algorithm for mining maximal frequent sequences us-ing samplus-ing, in ‘Tools with Artificial Intelligence’, ICTAI, 16th IEEE International Conference, pp. 156–165.

Mallach, E. G. (2000), Decision Support and Data Warehouse Systems, Irwin McGraw-Hill.

Manchanda, P., Ansari, A. and Gupta, S. (1999), ‘The ”shopping basket”: a model for multicategory purchase incidence decisions’, Marketing Science 18(2), 95–114.

Mannila, H. (1997), Methods and problems in data mining, in F. N. Afrati and P. G. Kolaitis, eds,

‘Database Theory - ICDT ’97, 6th International Conference, Delphi, Greece, January 8-10, 1997, Proceedings’, Vol. 1186 of Lecture Notes in Computer Science, Springer, pp. 41–55.

Mazanec, J. (2001), ‘Neural market structure analysis: Novel topology-sensitive methodology’, Euro-pean Journal of Marketing 35(7-8), 894–916.

McQueen, J. (1967), Some methods for classification and analysis of multivariate observations, in ‘Pro-ceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability’, University of California Press, Berkeley, CA, USA, pp. 281–297.

Merkle, E. (1981), Die Erfassung und Nutzung von Informationen ¨uber den Sortimentsverbund in Han-delsbetrieben, Vol. 11 of Schriften zum Marketing, Duncker and Humblot, Berlin.

Mild, A. and Reutterer, T. (2001), Collaborative filtering methods for binary market basket data analysis, in ‘AMT ’01: Proceedings of the 6th International Computer Science Conference on Active Media

Technology’, Springer-Verlag, London, UK, pp. 302–313.

Milligan, G. W. and Cooper, M. C. (1985), ‘An examination of procedures for determining the number of clusters in a data set’, Psychometrika 50(2), 159–179.

Mulhern, F. J. (1999), ‘Customer profitability analysis. measurement, concentration, and future direc-tions’, Journal of Interactive Marketing 13(1), 25–40.

M ¨uller-Hagedorn, L. (2005), Handelsmarketing, 4 edn, Kohlhammer, Stuttgart.

Neckel, P. and Knoblauch, B. (2005), Customer relationship analytics - Praktische Anwendung des Data Mining im CRM, Dpunkt-Verlag, Heidelberg.

Omiecinski, E. (2003), ‘Alternative interest measures for mining associations in databases.’, IEEE Trans-actions on Knowledgle and Data Engineering 15(1), 57–69.

Ordonez, C. (2003), Clustering binary data streams with k-means, in ‘DMKD ’03: Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery’, ACM, New York, NY, USA, pp. 12–19.

Ottmann, T. and Widmayer, P. (2002), Algorithmen und Datenstrukturen, 4 edn, Spektrum, Akademis-cher Verlag.

Papastefanou, G. (2001), The ZUMA data file version of the GfK ConsumerScan Household Panel, in G. Papastefanou, P. Schmidt, A. B örsch-Supan, H. L üdke and U. Oltersdorf, eds, ‘Social and Economic Analyses of Consumer Panel Data, Zentrum für Umfragen, Meinungen und Analysen (ZUMA)’, Mannheim, pp. 206–212.

Passingham, J. (1998), ‘Grocery retailing and the loyalty card’, Journal of Market Research Society 40(1), 55–63.

Piatetsky-Shapiro, G. (1991), Knowledge Discovery in Databases, AAAI/MIT Press, Cambridge, MA.

Poggenpohl, M. (1993), Verbundanalyse im Einzelhandel auf der Grundlage von Kundenkarteninfor-mationen – Eine empirische Untersuchung von Verbundbeziehungen zwischen Abteilungen, PhD thesis, Universit¨at M ¨unster.

R Development Core Team (2005), R: A language and environment for statistical computing, R Founda-tion for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.

URL: http://www.R-project.org

Rand, W. M. (1971), ‘Objective criteria for the evaluation of clustering methods’, Journal of the Ameri-can statistical association 66(336), 846–850.

Razi, F., Kausar, S., Khiyal, M. S. H. and Saeed, M. I. (2008), ‘Comparative study of randomized partition algorithm with CMA algorithm’, European Journal of Scientific Research 19(3), 482–

490.

Reichheld, F. F. (2001), The loyalty effect - the hidden force behind growth, profits, and lasting value, Harvard Business School, Boston, Mass.

Reinartz, W. J. and Kumar, V. (2000), ‘On the profitability of long-life customers in a noncontractual set-ting: An empirical investigation and implications for marketing’, Journal of Marketing 64(4), 17–

35.

Reischuk, R. (1990), Einf¨uhrung in die Komplexit¨atstheorie, 2 edn, Teubner, Stuttgart.

Reutterer, T., Hahsler, M. and Hornik, K. (2007), ‘Data mining und marketing am beispiel der explo-rativen warenkorbanalyse’, Marketing - Zeitschrift fr Forschung und Praxis (ZFP) 29(3), 163–179.

Reutterer, T. and Mild, A. (2003), ‘An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data’, Journal of Retailing and Consumer Ser-vices 10(3), 123–133.

Reutterer, T., Mild, A., Natter, M. et al. (2006), ‘A dynamic segmentation approach for targeting and customizing direct marketing campaigns’, Journal of Interactive Marketing 20(3), 43–57.

Ripley, B. D. (1996), Pattern recognition and neural networks, Cambridge University Press, Cambridge, UK.

Rossi, P. E., McCulloch, R. E. and Allenby, G. M. (1996), ‘The value of purchase history data in target marketing’, Marketing Science 15(4), 321–340.

Runte, M. (2000), Individualisierte Angebote mit Collaborative Filtering, PhD thesis, Christian-Albrechts-Universit¨at Kiel, Wiesbaden. DUV.

Russell, G. J. and Petersen, A. (2000), ‘Analysis of cross category dependence in market basket selec-tion’, Journal of Retailing 76(3), 367–392.

Rust, R. T., Lemon, K. and Zeithaml, V. A. (2004), ‘Return on marketing: Using customer equity to focus marketing strategy’, Journal of Marketing 68(1), 109–127.

Sakthi, U., Hemalatha, R. and Bhuvaneswaran, R. S. (2008), ‘Parallel and distributed mining of as-sociation rule on knowledge grid.’, Proceedings of World Academy of Science: Engineering &

Technology 32, 346 – 350.

Savasere, A., Omiecinski, E. and Navathe, S. B. (1995), An efficient algorithm for mining association rules in large databases, in ‘VLDB ’95: Proceedings of the 21th International Conference on Very Large Data Bases’, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 432–444.

Schnedlitz, P. and Kleinberg, M. (1994), ‘Verbundanalyse im Lebensmittelhandel’, Der Markt 33(1), 31–

39.

Schnedlitz, P., Reutterer, T. and Joos, W. (2001), Data-Mining und Sortimentsverbundanalyse im Einzel-handel, in H. Hippner, U. L. K ¨usters, M. Meyer and K. Wilde, eds, ‘Handbuch Data Mining im Marketing - Knowledge Discovering in Marketing Databases’, Viewag, Wiesbaden, pp. 951–970.

Schulz, H.-J. and Nocke, T. (2007), Maschinelle Datenanalyse im Informationszeitalter - K ¨onnen oder m ¨ussen wir ihr vertrauen?, in R. Langner, T. Luks, A. Schlimm, G. Straube and D. Thomaschke, eds, ‘Ordungen des Denkens: Debatten um Wissenschaftstheorie und Erkenntniskritik’, 2 edn,

In document Building a Data Mining Framework for Target Marketing (Page 158-184)