underwear

The dataset comes from a large survey conducted by a South Korean underwear manufacturer in 1997. 664 South Korean consumers were asked to provide responses for three multiple-choice items: attributes when considering a brand of underwear to purchase (15 attributes), preferred brand of underwear (8 brands) and consumer age (3 levels).

datasets

A class of methods that combine dimension reduction and clustering of continuous or categorical data. For continuous data, the package contains implementations of factorial K-means (Vichi and Kiers 2001; <DOI:10.1016/S0167-9473(00)00064-5>) and reduced K-means (De Soete and Carroll 1994; <DOI:10.1007/978-3-642-51175-2_24>); both methods that combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means (Hwang, Dillon and Takane 2006; <DOI:10.1007/s11336-004-1173-x>), i-FCB (Iodice D'Enza and Palumbo 2013, <DOI:10.1007/s00180-012-0329-x>) and Cluster Correspondence Analysis (van de Velden, Iodice D'Enza and Palumbo 2017; <DOI:10.1007/s11336-016-9514-0>), which combine multiple correspondence analysis with K-means.

Angelos Markos

clustrd

Methods for Joint Dimension Reduction and Clustering

underwear function

A data frame with 664 observations on the following variables.<dl class="dl-horizontal">
 <dt><code>brand</code></dt><dd>factor indicating underwear brand, with levels <code>1. BYC</code>, <code>2. TRY</code>, <code>3. VICMAN</code>, <code>4. James Dean</code>, <code>5. Michiko-London</code>, <code>6. Benetton</code>, <code>7. Bodyguard</code>, <code>8. Calvin Klein</code>.</dd>
 <dt><code>atts</code></dt><dd>factor indicating underwear attributes, with levels <code>1. Comfortable</code>, <code>2. Smooth</code>, <code>3. Superior fabrics</code>, <code>4. Reasonable price</code>, <code>5. Fashionable design</code>, <code>6. Favorable advertisements</code>, <code>7. Trendy color</code>, <code>8. Good design</code>, <code>9. Various colors</code>, <code>10. Elastic</code>, <code>11. Store is near</code>, <code>12. Excellent fit</code>, <code>13. Design quality</code>, <code>14. Youth appeal</code>,
<code>15. Various sizes</code>.</dd>
 <dt><code>age</code></dt><dd>ordered factor indicating consumer's age, with levels <code>1. 10-29</code>, <code>2. 30-49</code>, <code>3. 50 and over</code>.</dd>
 </dl>

Format

South Korean Underwear — underwear

A data frame with 664 observations on the following variables.<dl class='dl-horizontal'>
 <dt><code>brand</code></dt><dd>factor indicating underwear brand, with levels <code>1. BYC</code>, <code>2. TRY</code>, <code>3. VICMAN</code>, <code>4. James Dean</code>, <code>5. Michiko-London</code>, <code>6. Benetton</code>, <code>7. Bodyguard</code>, <code>8. Calvin Klein</code>.</dd>
 <dt><code>atts</code></dt><dd>factor indicating underwear attributes, with levels <code>1. Comfortable</code>, <code>2. Smooth</code>, <code>3. Superior fabrics</code>, <code>4. Reasonable price</code>, <code>5. Fashionable design</code>, <code>6. Favorable advertisements</code>, <code>7. Trendy color</code>, <code>8. Good design</code>, <code>9. Various colors</code>, <code>10. Elastic</code>, <code>11. Store is near</code>, <code>12. Excellent fit</code>, <code>13. Design quality</code>, <code>14. Youth appeal</code>,
<code>15. Various sizes</code>.</dd>
 <dt><code>age</code></dt><dd>ordered factor indicating consumer's age, with levels <code>1. 10-29</code>, <code>2. 30-49</code>, <code>3. 50 and over</code>.</dd>
 </dl>

underwear: South Korean Underwear

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Examples