For each question, True (T) or False ( \\( F \\) ) as appropriate. (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 \\( \\mathrm{X} \\) and also contains \\( \\mathrm{Y} \\) is a confidence