Unsupervised learning refers to machine learning without pre-determined targets and without any reward from the environment. The machine learning tries to recognize patterns in the input data that deviate from the structureless noise. An artificial neural network is based on the similarity to the input values and adapts the weights accordingly. There are several things that can be learned. Automatic segmentation (clustering) or principal component analysis of data for dimension reduction are popular.
Benefits of Unsupervised Larning
Despite the smaller amount of information available in the data, unsupervised learning can make a significant contribution to machine learning. At first, the smaller amount of data available seems like a disadvantage compared to supervised learning, which can focus on a specific goal by controlling it through rewards and metrics.
However, unsupervised learning offers the advantage that it is also possible to work with data for which no target values exist. Supervised learning would reach its limits here. With fewer limitations on how much data can be used, unsupervised learning can also identify new structures in data. In this process, conclusions are drawn from the existing data regarding recurring patterns, so that further data can be structured from these conclusions in the future. Due to the lower limitations in terms of desired target values, commonalities can be identified that were not previously considered.
One of the main tasks of unsupervised learning is to find clusters in unstructured data. This is an attempt to find commonalities between the individual entries of a data set and to form clusters based on the similarities.
There are different ways of dividing them into these clusters. Cluster analysis distinguishes between hierarchical, partitioning, density-based and grid-based clustering, among other things. In some cases, these approaches differed significantly in their results with regard to the data considered, so that the optimal algorithm always comes down to a case-by-case decision. To analyze the quality of the clusters created, there are various metrics to evaluate the individual algorithms.
In addition to the division into individual clusters, individual outliers in a data set can also be detected by means of unsupervised learning. The aim is to identify data that differs from previously defined normal behavior.
The implementation of this outlier detection differs greatly between the individual algorithms. A popular algorithm in this area is the Isolation Forest algorithm. These algorithms of unsupervised learning can be found in various application scenarios in practice.
In the area of security, it is used, for example, within intrusion detection systems. In doing so, they are supposed to detect attackers by means of behavior that deviates from the norm. The models were previously trained on normal data traffic.
- Can see what human cannot visualize.
- Can dig hidden patterns which has widespread applications in real-time.
- Less complex compared to the supervised learning.
- Easier to obtain unlabeled data.