SAP HANA Predictive Analysis Library (PAL)

Expected Result
DOCTOPICDIST_TBL:
3.1.11 Self-Organizing Maps
Self-organizing feature maps (SOMs) are one of the most popular neural network methods for cluster analysis.
They are sometimes referred to as Kohonen self-organizing feature maps, after their creator, Teuvo Kohonen,
or as topologically ordered maps. SOMs aim to represent all points in a high-dimensional source space by
points in a low-dimensional (usually 2-D or 3-D) target space, such that the distance and proximity
relationships are preserved as much as possible. This makes SOMs useful for visualizing low-dimensional
views of high-dimensional data, akin to multidimensional scaling.
SOMs can also be viewed as a constrained version of k-means clustering, in which the cluster centers tend to
lie in low-dimensional manifold in the feature or attribute space. The learning process mainly includes three
steps:
1. Initialize the weighted vectors in each unit.
2. Select the Best Matching Unit (BMU) for every point and update the weighted vectors of BMU and its
neighbors.
3. Repeat Step 2 until convergence or the maximum iterations are reached.
An important variant is batch SOM, which updates the weighted vectors only at the end of every learning
epoch. It requires that the whole set of training data is present, and is independent on the order of input
vectors.
The SOM approach has many applications such as virtualization, web document clustering, and speech
recognition.
Prerequisites
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The first column of the input data is an ID column and the other columns are of integer or double data
type.
The input data does not contain null value. The algorithm will issue errors when encountering null values.
SAP HANA Predictive Analysis Library (PAL)
PAL Functions P U B L I C 103