$($T$/$F$)$ In data exploration, we perform data cleaning and data integration and store resulting data in data warehouse.

$($T$/$F$)$ Temperature in Kelvin, counts, age, mass, length, electrical current are the examples of ordinal attribute type. $($T$/$F$)$

$($T$/$F$)$ In clustering analysis, partitioning methods create a hierarchical decomposition of the given set of data objects.

$($T$/$F$)$ Numeric prediction predicts categorical class labels.

$($T$/$F$)$ Apriori algorithm uses support and confidence metrics to create association rules

$($T$/$F$)$ Semi$-$supervised learning attempts to improve the accuracy of supervised learning by exploiting information in unlabeled data.

$($T$/$F$)$ For finding frequent pattern, conditional probability that is a transaction of having X and also contains Y is a confidence