A Review of Kohonen’s Self-Organizing Map (Kohonen’s Map)
Kohonen’s Self-Organizing Map (Kohonen’s Map) is a type of Artificial Neural Network (ANN) developed by Professor Teuvo Kohonen. This type of network is also known as a self-organizing feature map and uses unsupervised learning to classify information into clusters. The network uses a competitive learning approach in which each neuron is connected to all the neurons in its neighboring layer, creating a topological arrangement of nodes. This allows the neurons to compete against each other to identify the most relevant groupings of data. The major advantage of this model is its ability to learn from the input data and to organize the input data into categories that represent the underlying data structure. It also has the ability to cluster the data into multiple layers. This makes it suitable for a wide range of applications such as pattern recognition, clustering, classification and data exploration.
There are two distinct phases in the functioning of a Kohonen’s Map. In the first phase, the model learns from the input data by using competitive learning and the neurons are self organized into clusters. During this learning phase, the weights of the neurons are adjusted to minimize the errors. In the second phase, the neurons are presented with the input data and the data is classified into the clusters that were learned during the first phase.
Kohonen’s Map is a popular ANN model used in many data mining applications. It is used in applications such as recommendation systems and clustering. It has also been used in classification and pattern recognition applications. Additionally, It can be used to explore high-dimensional datasets or identify patterns that are not easily visible.
The architecture of a Kohonen’s Map is fairly simple. It consists of a single layer of neurons, which are connected to each other through a set of weights. The neurons self-organize by learning from the input data and converging towards the region of highest weight. Each neuron responds to the inputs from the previous neurons and attempts to reconstruct the features of the input data.
One of the biggest advantages of the Kohonen’s Map is its ability to learn from the input data and to organize the input data into clusters. This makes it highly suitable for data exploration and mining. Additionally, the ability to cluster the data into multiple layers makes it suitable for applications such as image recognition, speech recognition and natural language processing.
Kohonen’s Map also has some drawbacks. It relies heavily on the input data and cannot learn from previously unseen patterns. Additionally, its learning process is slow and requires large amounts of data to produce good results. Nonetheless, this ANN model still remains one of the most popular and widely used methods of data mining.
