Restricted Boltzmann Machines (RBMs) can identify patterns in a CSV file without the user specifying any conditions. They are well fitted for generating, "distributed and graded representations," of a, "complex set of features composing real high-dimensional data is crucial for achieving high performance in machine–learning tasks."1
Because the CSV format is specifically designed to represent instances in rows and a static set of attributes in columns, the set up of the training is straightforward. If the goal is to identify temporal patterns, a windowing strategy may be required.
K-RBMs are a merger of k-mean approaches with RBMs. The choice of approach has much to do with what kinds of patterns are sought. The term pattern can apply to simple trends in numbers over time, common patterns found in textual columns, or complex patterns inferred from multiple columns.
 Emergence of Compositional Representations in Restricted Boltzmann Machines, J. Tubiana, R. Monasson, 2017)
 Learning Multiple Non-Linear Sub-Spaces using K-RBMs, Siddhartha Chandra, Shailesh Kumar & C. V. Jawahar