Data Clustering

The problem of Data Clustering has been extensively examined in the fields of data mining and machine learning, because of its applications to summarization, segmentation and target marketing. When there isn't labeled information, clustering is considered one of the best models to interpret data. In a few words the problem of clustering may be described as:

Given an unlabeled dataset, the clustering algorithms part it into a set of groups which are as similar as possible

There are a huge number of ways to define "similarity", for example, it can be defined using euclidean distance between points, where the closest points are similar to each other; or based on a probabilistic generative mechanism, where similarity is related to the probability of the points coming from the same distribution. The most common applications of clustering models are:

Collaborative Filtering: It can be used to group similar users or items based on their preferences by ratings. Once these clusters are identified, collaborative filtering techniques can be used within each cluster to make personalized recommendations. For example, Netflix uses a recommendation engine, which clusters users with similar viewing habits and provides personalized show suggestions.

Customer Segmentation: Similarity to Collaborative Filtering, it creates groups of similar customers in the data, the difference is that instead of using rating information, arbitrary attributes about the objects can be used.  For example, in an e-commerce campaign, the company divides the customer base into distinct segments based on various attributes such as age, gender, purchasing behavior, and preferences.

Data Summarization: Some clustering models are related with dimensionality reduction methods, which can be considered a form of data summarization and may be employed to group similar documents together, allowing for topic modeling and the creation of compact data representations.

Multimedia Data Analysis: It can be used to identify patterns and group similar items based on visual or audio features. It is suited for use in data compression and retrieval by reducing redundancy, and simplify complex data. In image processing, clustering algorithms like K-means can be used to segment regions or identify similar images. In audio analysis, clustering can help to identify music genres. 

Social Network Analysis: It determines the communities in a specific social network, which provides an important understanding of consumers and active people. It also has applications in social network summarization, which is essential in personalized recommendations, anomaly detection, nodes identification, etc. For example, in a social media platform, clustering algorithms can be employed to identify communities with similar interests, connections, or interactions.

Before implementing clustering methods, it's necessary to preprocess the datasets. In particular, the feature selection phase is an important step which is needed in order to improve the results and quality of the underlying clustering. Not all features has the same information to find the clusters, because some may be more noisy than others, for this reason it's important to include a preprocessing phase in which noisy and irrelevant features are removed.  Dimensionality reduction algorithms can be employed for this tasks.