{"id":851,"date":"2014-07-28T11:23:54","date_gmt":"2014-07-28T15:23:54","guid":{"rendered":"https:\/\/secure.compusense.com\/?p=851"},"modified":"2018-03-16T11:26:02","modified_gmt":"2018-03-16T15:26:02","slug":"the-role-of-imputation-in-clustering-bib-data","status":"publish","type":"post","link":"https:\/\/secure.compusense.com\/research\/the-role-of-imputation-in-clustering-bib-data\/","title":{"rendered":"The Role of Imputation in Clustering BIB Data"},"content":{"rendered":"

Clustering consumer data reveals important information to help refine products for specific market segments. There are compelling reasons to use incomplete block designs to collect consumer data; however, this presents the challenge of dealing with missing data. The purpose of this workshop is to investigate the effect of different imputation techniques on the results of cluster analysis of balanced-incomplete-block (BIB) data.<\/p>\n

BIB designs and possible ways to incorporate information from sensory data into the design (cf. Browne et al., 2013) will be reviewed. Then, commonly used imputation and clustering approaches will be discussed. After this background material has been covered, expectation-maximization (EM) algorithm-based imputation will be presented.<\/p>\n

The remainder of the workshop will be interactive and hands on. Participants will be given two data sets (provided by Compusense Inc.) resulting from sensory informed BIB designs. Using R software, which will also be provided, participants will be invited to use EM algorithm-based imputation and compare the results to other approaches.<\/p>\n

\"\"<\/p>\n

Browne, R.P., Franczak, B., Findlay, C.J., & McNicholas, P.D. (2014). The Role of Imputation in Clustering BIB Data. In: 12th Sensometrics Meeting \u2013 Tutorial Day. 28 July. Chicago, Illinois, USA.<\/p>\n","protected":false},"excerpt":{"rendered":"

Clustering consumer data reveals important information to help refine products for specific market segments. There are compelling reasons to use incomplete block designs to collect consumer data; however, this presents the challenge of dealing with missing data. The purpose of this workshop is to investigate the effect of different imputation techniques on the results of cluster analysis of balanced-incomplete-block (BIB)<\/p>\n

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