====== Clustering ======

see [[Biopsychosocial clustering]] 

The task of grouping a set of objects in such a way that objects in the same group (called a [[cluster]]) are more similar (in some sense) 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, bioinformatics, data compression, and computer graphics.

Given a set of data points, we can use a [[cluster algorithm]] to classify each data point into a specific group. 

[[Cluster analysis]] or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a [[cluster]]) are more similar (in some sense) to each other than to those in other groups (clusters).
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: These algorithms are used to group similar data points together based on their intrinsic properties. Common clustering algorithms include:

K-Means Clustering
Hierarchical Clustering
DBSCAN
Gaussian Mixture Models (GMM)
Dimensionality Reduction: These algorithms reduce the number of features in a dataset while retaining important information. Common dimensionality reduction techniques are:

Principal Component Analysis (PCA)
t-Distributed Stochastic Neighbor Embedding (t-SNE)
Linear Discriminant Analysis (LDA)
Anomaly Detection: Anomaly detection algorithms identify unusual data points that do not conform to expected patterns. Algorithms in this category include:

Isolation Forest
One-Class SVM
Local Outlier Factor (LOF)
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Reliable intraoperative [[delineation]] of [[tumor]] from healthy [[brain tissue]] is essentially based on the neurosurgeon's visual aspect and [[tactile perception]] of the considered [[tissue]], which is-due to inherent low brain consistency contrast-a challenging task. Development of an intelligent [[artificial]] intraoperative tactile perception will be a relevant task to improve the safety during surgery, especially when-as for neuroendoscopy-[[tactile perception]] will be damped or-as for surgical robotic applications-will not be a priori existent. 

Stroop et al. from the Department of Neurosurgery, Academic [[Hospital Cologne-Merheim]], Department of Engineering Technology (INDI), Vrije Universiteit [[Brussels]], Belgium, presented the enhancements and the evaluation of a [[tactile sensor]] based on the use of a [[piezoelectric]] tactile sensor.

A robotic-driven piezoelectric bimorph sensor was excited using multisine to obtain the frequency response function of the contact between the sensor and fresh ex vivo porcine tissue probes. Based on load-depth, relaxation and creep response tests, viscoelastic parameters E1 and E2 for the elastic moduli and η for the viscosity coefficient have been obtained allowing tissue classification. Data analysis was performed by a multivariate [[cluster algorithm]].

Cluster algorithm assigned five clusters for the assignment of white matter, basal ganglia and thalamus probes. Basal ganglia and white matter have been assigned to a common cluster, revealing a less discriminatory power for these tissue types, whereas thalamus was exclusively delineated; gray matter could even be separated in subclusters.

Bimorph-based, multisine-excited tactile sensors reveal a high sensitivity in ex vivo tissue-type differentiation. Although, the sensor principle has to be further evaluated, these data are promising
((Stroop R, Nakamura M, Schoukens J, Oliva Uribe D. Tactile sensor-based
real-time [[clustering]] for tissue differentiation. Int J Comput Assist Radiol Surg.
2018 Oct 6. doi: 10.1007/s11548-018-1869-5. [Epub ahead of print] PubMed PMID:
30293172.
)).