Question

Please provide me algorithm for Self organizing Maps for

(a) filling of missing value ( i.e. for regression problem)

(b) for detection of outliers (i.e. for clustering problem)

Please write the Algorithm in step wise (with 100% plagiarism free)

Answer 1

The Self-Organizing Map (SOM) is a neural network useful for visualizing high-dimensional data. Algorithms using SOM can be created for filling in missing values in a dataset (regression) and detecting outliers (clustering). The outlined procedures detail the adaptation of SOM for these distinct data processing challenges.

Step-by-step explanation:

The Self-Organizing Map (SOM) algorithm is a type of unsupervised neural network used for data visualization and clustering. SOM can be adapted for regression problems, such as filling in missing values, and for clustering problems, such as detecting outliers. Here are the step-by-step algorithms:

Filling in Missing Values with SOM

1. Initialize the SOM with random weights and organize the grid topology.
2. Present the complete data instances to the SOM and train it, using a competitive learning algorithm.
3. Once the SOM has been trained, present it with data instances that have missing values.
4. The SOM will classify these instances into their closest neuron based on the available features.
5. Use the weights of the neuron as the predicted values for the missing features.
6. Iterate through steps 2 to 5 until convergence or a predetermined number of epochs is reached.

Detection of Outliers with SOM

1. Initialize the SOM with random weights and organize the grid topology.
2. Train the SOM with a competitive learning algorithm using the entire data set.
3. After training, calculate the quantization error of each data instance, which reflects the distance between it and the winning neuron.
4. Instances with significantly higher quantization errors than the average error are flagged as potential outliers.
5. Further analyze these potential outliers to confirm whether they are true outliers or merely data points with high variability.

The algorithms provided use the principles of SOM for specific tasks in a regression and clustering context, and can be custom tailored based on the specific dataset and problem at hand.