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diff --git a/markup/non-pod-samples/democratizing_innovation.eric_von_hippel.non-pod-sample.sst b/markup/non-pod-samples/democratizing_innovation.eric_von_hippel.non-pod-sample.sst index 14dccfd..686e8a2 100644 --- a/markup/non-pod-samples/democratizing_innovation.eric_von_hippel.non-pod-sample.sst +++ b/markup/non-pod-samples/democratizing_innovation.eric_von_hippel.non-pod-sample.sst @@ -30,18 +30,18 @@ identifier: # 338'.064-dc22 2004061060 links: [ - "{ Democratizing Innovation }http://web.mit.edu/evhippel/www/democ1.htm", - "{ Eric von Hippel }http://evhippel.mit.edu/", - "{ @ Wikipedia }http://en.wikipedia.org/wiki/Democratizing_Innovation", - "{ Democratizing Innovation @ Amazon.com }http://www.amazon.com/Democratizing-Innovation-Eric-Von-Hippel/dp/0262720477", - "{ Democratizing Innovation @ Barnes & Noble }http://search.barnesandnoble.com/booksearch/isbnInquiry.asp?isbn=9780262720472" + "{ Democratizing Innovation }https://web.mit.edu/evhippel/www/democ1.htm", + "{ Eric von Hippel }https://evhippel.mit.edu/", + "{ @ Wikipedia }https://en.wikipedia.org/wiki/Democratizing_Innovation", + "{ Democratizing Innovation @ Amazon.com }https://www.amazon.com/Democratizing-Innovation-Eric-Von-Hippel/dp/0262720477", + "{ Democratizing Innovation @ Barnes & Noble }https://search.barnesandnoble.com/booksearch/isbnInquiry.asp?isbn=9780262720472" ] make: breaks: "new=:B,C; break=1" texpdf_font: "Liberation Sans" - home_button_image: "{di_evh.png }http://evhippel.mit.edu/" - footer: "{Eric von Hippel}http://evhippel.mit.edu/" + home_button_text: "{Original @ MIT Press}https://mitpress.mit.edu/9780262720472/democratizing-innovation/; {E. von Hippel}https://evhippel.mit.edu/" + footer: "{Eric von Hippel}https://evhippel.mit.edu/" :A~ @title-author-date @@ -3873,7 +3873,9 @@ In this book I have explored how and why users, individually and in firms and in 1. Cluster analysis does not specify the "right" number of clusters---it simply segments a sample into smaller and smaller clusters until the analyst calls a halt. Determining an appropriate number of clusters within a sample can be done in different ways. Of course, it always possible to say that "I only want to deal with three market segments, so I will stop my analysis when my sample has been segmented into three clusters." More commonly, analysts will examine the increase of squared error sums of each step, and generally will view the optimal number of clusters as having been reached when the plot shows a sudden "elbow" (Myers 1996). Since this technique does not incorporate information on remaining within-cluster heterogeneity, it can lead to solutions with a large amount of within-cluster variance. The "cubic clustering criterion" (CCC) partially addresses this concern by measuring the within-cluster homogeneity relative to the between-cluster heterogeneity. It suggests choosing the number of clusters where this value peaks (Milligan and Cooper 1985). However, this method appears to be rarely used: Ketchen and Shook (1996) found it used in only 5 of 45 segmentation studies they examined. -2. http://groups-beta.google.com/group/comp.infosystems.www.servers.unix +2. https://groups.google.com/g/comp.infosystems.www.servers.unix + +% 2. http://groups-beta.google.com/group/comp.infosystems.www.servers.unix 3. http://modules.apache.org/ |