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Understanding these limitations will allow you to select the appropriate method Alternatively, you could decide to restrict the variables used in the method or perform a transformation on the data Data set size limit: There are methods that only work with data sets less than a certain size Others work best with data sets over a certain size Understanding the limitations placed on the number of observations and/or number of variables helps in the selection of particular methods In situations where the data set is too large to process, one solution would be to segment the data prior to grouping Interpretable and actionable: Certain grouping methods generate results that are easy to interpret, whereas other methods require additional analysis to interpret the results How the grouping results will be used in uences which grouping methods should be selected.

Overlapping groups: In certain grouping methods, observations can only fall in one group There are other grouping methods where the same observation may be a member of multiple groups A related topic to grouping is the identi cation of outliers, that is, observations that do not look like anything else Single or small numbers of observations that fall into groups on their own are considered outliers A data set where most of the observations fall into separate groups would be described as diverse To create a diverse subset, representative observations from each group may be selected Other methods for assessing outliers are discussed at the end of the chapter This chapter describes three popular methods for grouping data sets: clustering, associative rules, and decision trees They cover different criteria for generating groups as well as supervised and unsupervised approaches.

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All approaches have advantages and disadvantages, and all provide different insights into the data It is often informative to combine these grouping methods with other data analysis/data mining techniques, such as hypothesis tests to evaluate any claims made concerning the groups The different methods have parameters that can be modi ed to optimize the results and these are described..

At the start of this chapter, I mentioned an all-too-common lament: a company opened its site and nobody came. Let me suggest two reasons why people might not come to your online party. First, there was little or no outreach to the social web; and second, the content was not compelling enough. You need a de nite plan for attracting and retaining community members. And that s where your marketing knowledge and skills come in. Marketing to the social web does not mean forgetting everything you ve learned. It does mean using a new toolkit or approach to build on what you already know. You ll need new and different perspectives on how you connect with and relate to consumers, but the basics of good strategic and tactical marketing communications don t change. You re trying to generate leads, produce revenue, and exert in uence; you re trying to generate brand awareness, induce trial, and build customer loyalty. It s the old lather, rinse, and repeat from Marketing 101. None of this is particularly revolutionary, but it s absolutely critical. You already have some idea of how to address many of the questions raised by marketing to the social web. Now it s time to put your skills to work by recruiting members for your online community. You recruit online community members the same way you do in the of ine world, but it is much easier and richer online. A good starting place is to think about the reasons why people join online communities at all. According to Compete Inc.,4 there are four reasons: 1. Meet people. Some 78 percent of the people who visit online communities join them to communicate with others, either colleagues or new acquaintances with whom they develop relationships.

the same cues to deceit is not the same as showing the same patterns of behaviour. People from different ethnic backgrounds or cultures often display different behavioural patterns (Matsumoto, 2006). For example, Caucasians tend to look conversation partners in the eye more than African Americans (Fugita, Wexley, & Hillery, 1974; Ickes, 1984; Johnson, 2006; LaFrance & Mayo, 1976; Smith, 1983). However, truth tellers and liars do not differ in eye contact in Caucasian participants, neither do truth tellers and liars differ in eye contact in those non-Caucasian groups that have been examined (Sitton & Grif n, 1981; Vrij & Winkel, 1991). In other words, gaze behaviour is in uenced by culture, but not by veracity. Differences in behaviour displayed by people from different ethnic origins or cultures may easily lead to errors in lie detection as I will discuss in 6.

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