Cluster analysis

Problems
Classification Clustering Regression Anomaly detection Association rules Reinforcement learning Structured prediction Feature learning Online learning Semi-supervised learning Unsupervised learning Grammar induction
Supervised learning (classification • regression)
Decision trees Ensembles (Bagging,Boosting, Random forest) k-NN Linear regression Naive Bayes Neural networks Logistic regression Perceptron Support vector machine (SVM) Relevance vector machine (RVM)
Clustering
BIRCH Hierarchical k-means Expectation-maximization (EM) DBSCAN OPTICS Mean-shift
Dimensionality reduction
Factor analysis CCA ICA LDA NMF PCA t-SNE
Structured prediction
Graphical models (Bayes net, CRF, HMM)
Anomaly detection
k-NN Local outlier factor
Neural nets
Autoencoder Deep learning Multilayer perceptron RNN Restricted Boltzmann machine SOM Convolutional neural network
Theory
Bias-variance dilemma Computational learning theory Empirical risk minimization PAC learning Statistical learning VC theory
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Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called acluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning,pattern recognition, image analysis, information retrieval, and bioinformatics.

Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It will often be necessary to modify data preprocessing and model parameters until the result achieves the desired properties.

Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek βότρυς "grape") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals.

Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 andRobert Tryon in 1939^] and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology.

see more: https://en.wikipedia.org/wiki/Cluster_analysis