Final answer:
Measurement techniques are fundamental in data mining for tasks like classification, regression, and clustering. They help in summarizing and visualizing multivariate data and building classifications through methods such as PCA, CA, MDS, and clustering analysis.
Step-by-step explanation:
The student's question points to the importance of measurement techniques in the field of data mining. These measurements often form the basis of various data mining tasks such as classification, regression, and clustering. Methods such as ordination, which involve summarizing multivariate data into lower dimensions, make use of distances like Euclidean or Bray-Curtis to visualize similarity. Important techniques that provide insights into data include principal components analysis (PCA), correspondence analysis (CA), and multidimensional scaling (MDS). Cluster analysis is another technique that can be applied to data to build classifications, and it can be hierarchical, divisive, agglomerative, monothetic, or polythetic. These methods help to identify patterns, summarize variations, and condense explanatory variables into simpler gradients.