1. A method for assessing an individual's psychological state based on brain function data, comprising: a) obtaining brain function data from said individual using one or more brain imaging techniques; b) analysing, through an AI platform, said brain function data to determine one or more features indicative of said individual's psychological state; c) comparing said one or more features to one or more reference values corresponding to said individual's psychological state; and d) generating a report indicating said individual's psychological state based on said comparison.

2. The method of claim 1, wherein said brain function data comprises electroencephalography (EEG) data, functional magnetic resonance imaging (fMRI) data, or a combination thereof.

3. The method of claim 1, wherein the AI platform deploy machine learning based algorithm selected from k-nearest neighbour (kNN) algorithm, a Naïve Bayes algorithm, an algorithm employing decision trees, a linear regression algorithm, a support vector machine (SVM) and/or neural networks.

4. The method of claim 1, wherein said report is used to assist in diagnosing said disorder and/or developing a treatment plan.

5. A system for assessing an individual's psychological state based on brain function data, comprising: a) one or more brain imaging devices for obtaining brain function data from said individual; b) a processor configured to analyse said brain function data using an AI platform to determine one or more features indicative of said individual's psychological state, compare said one or more features to one or more reference values corresponding to said individual's psychological state, and generate a report indicating said individual's psychological state based on said comparison; and c) a display device for displaying said report.

6. The system of claim 5, wherein said report indicating said individual's psychological state is based on said one or more features and said psychological test results.

7. The system of claim 6, wherein said individual is a patient suffering from a psychiatric or neurological disorder, and wherein said report is used to assist in diagnosing said disorder and/or developing a treatment plan.

8. The system of claim 5, wherein the AI platform deploy one or more algorithm selected from Non-Linear Regression, Clustering, Hierarchical Clustering – Agglomerative, Hierarchical Clustering – Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization) Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF) and combination thereof.   CLINICAL RESPONSE DATA BASED PATIENT'S PSYCHOLOGICAL PERFORMANCE ANALYSIS Abstract The present disclosure may include a method for assessing the psychological state of an individual based on data pertaining to the individual's brain function. This method may include techniques for obtaining brain function data from the individual, such as one or more imaging techniques for the brain. Analyzing the data from the individual's brain activity using an AI platform in order to identify one or more characteristics that are indicative of the individual's psychological condition. In certain embodiments, there is also the possibility of producing a report that, on the basis of the comparison, indicates the individual's psychological condition. , Claims:Claims :

1. A method for assessing an individual's psychological state based on brain function data, comprising: a) obtaining brain function data from said individual using one or more brain imaging techniques; b) analysing, through an AI platform, said brain function data to determine one or more features indicative of said individual's psychological state; c) comparing said one or more features to one or more reference values corresponding to said individual's psychological state; and d) generating a report indicating said individual's psychological state based on said comparison.

2. The method of claim 1, wherein said brain function data comprises electroencephalography (EEG) data, functional magnetic resonance imaging (fMRI) data, or a combination thereof.

3. The method of claim 1, wherein the AI platform deploy machine learning based algorithm selected from k-nearest neighbour (kNN) algorithm, a Naïve Bayes algorithm, an algorithm employing decision trees, a linear regression algorithm, a support vector machine (SVM) and/or neural networks.

4. The method of claim 1, wherein said report is used to assist in diagnosing said disorder and/or developing a treatment plan.

5. A system for assessing an individual's psychological state based on brain function data, comprising: a) one or more brain imaging devices for obtaining brain function data from said individual; b) a processor configured to analyse said brain function data using an AI platform to determine one or more features indicative of said individual's psychological state, compare said one or more features to one or more reference values corresponding to said individual's psychological state, and generate a report indicating said individual's psychological state based on said comparison; and c) a display device for displaying said report.

6. The system of claim 5, wherein said report indicating said individual's psychological state is based on said one or more features and said psychological test results.

7. The system of claim 6, wherein said individual is a patient suffering from a psychiatric or neurological disorder, and wherein said report is used to assist in diagnosing said disorder and/or developing a treatment plan.

8. The system of claim 5, wherein the AI platform deploy one or more algorithm selected from Non-Linear Regression, Clustering, Hierarchical Clustering – Agglomerative, Hierarchical Clustering – Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization) Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF) and combination thereof.

Description:CLINICAL RESPONSE DATA BASED PATIENT'S PSYCHOLOGICAL PERFORMANCE ANALYSIS
Field of the Invention
[0001] The present invention relates generally to bio-signal collection methods, more particularly to a system and method for evaluating clinical response data based assessing a psychological performance analysis by using machine learning.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Clinical response data refers to the objective measures of treatment effectiveness in patients. These measures are typically based on medical and behavioural outcomes, such as reduction of symptoms, improvement in functioning, and overall quality of life. Patient's psychological performance refers to the psychological factors that contribute to the patient's overall well-being, including mood, cognition, and behaviour.
[0004] The use of clinical response data to assess patient's psychological performance is an important area of research in clinical psychology and psychiatry. It allows clinicians to objectively measure treatment effectiveness and make informed decisions about patient care.
[0005] The biosignals produced by the human brain, such as electrical patterns, can be detected or tracked using an electroencephalogram (EEG). By using instruments like an EEG, these electrical patterns, or brainwaves, may be measured. An EEG typically records brainwaves as analogue data. After then, these brainwaves can either be studied in their original analogue form or after being converted from analogue to digital.
[0006] Many real-world uses can be made of the measuring and analysis of biosignals such brainwave patterns. As an illustration, brain computer interfaces (BCI) have been created, enabling users to operate gadgets and computers via brainwave impulses.
[0007] Various technological solutions (e.g., mapping cognitive to functional ability, brain function test system and its device, etc.) are disclosed in patent literature. Few of the exemplary documents are discussed below.
[0008] US9367666B2 (By: EMBIC) relates to methods, systems, and apparatus, including medium-encoded computer program products, for mapping cognitive to functional ability include receiving data regarding assessments of a cognitive ability and assessments of a functional ability; processing the received data to generate a map of one or more cognitive processes underlying the cognitive ability to a continuous-valued measure of the functional ability; and storing the generated map on a computer-storage medium to be used by a computer device in continuous-valued assessments of the functional ability.
[0009] KR102346696B1 (By: JASMINES BIOTECH) discloses a method to test brain function based on measuring saccadic eye movement, which comprises a plurality of modules of eye movement tasks, eye movement data collection and data storage and analysis module. Among them, real-time analysis of head and eye position data is used in eye movement data collection to compensate for the impact of head movement on eye position, and the result is more accurate. also discloses a device using the system, which can be used for determining various neurodegenerative diseases.
[00010] However, the technological solutions for brain function processing suffers from various limitations such as, inaccuracy, etc. Consequently, the present invention relates with evaluating clinical response data based assessing a psychological performance analysis by using machine learning.
[00011] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
[00012] The present invention relates generally to bio-signal collection methods, more particularly to a system and method for evaluating clinical response data based assessing a psychological performance analysis by using machine learning.
[00013] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00014] The following paragraphs provide additional support for the claims of the subject application.
[00015] Embodiments of the present disclosure may include a method for assessing an individual's psychological state based on brain function data, including obtaining brain function data from the individual using one or more brain imaging techniques. Embodiments may also include analysing, through an AI platform, the brain function data to determine one or more features indicative of the individual's psychological state. Embodiments may also include comparing the one or more features to one or more reference values corresponding to the individual's psychological state. Embodiments may also include generating a report indicating the individual's psychological state based on the comparison.
[00016] In some embodiments, the brain function data may include electroencephalography (EEG)data, functional magnetic resonance imaging (fMRI)data, or a combination thereof. In some embodiments, the AI platform deploy machine learning based algorithm selected from k-nearest neighbour (kNN)algorithm, a Naïve Bayes algorithm, an algorithm employing decision trees, a linear regression algorithm, a support vector machine (SVM)and/or neural networks. In some embodiments, the report may be used to assist in diagnosing the disorder and/or developing a treatment plan.
[00017] Embodiments of the present disclosure may also include a system for assessing an individual's psychological state based on brain function data, including one or more brain imaging devices for obtaining brain function data from the individual. Embodiments may also include a processor configured to analyse the brain function data using an AI platform to determine one or more features indicative of the individual's psychological state, compare the one or more features to one or more reference values corresponding to the individual's psychological state, and generate a report indicating the individual's psychological state based on the comparison. Embodiments may also include a display device for displaying the report.
[00018] In some embodiments, the report indicating the individual's psychological state may be based on the one or more features and the psychological test results. In some embodiments, the individual may be a patient suffering from a psychiatric or neurological disorder. In some embodiments, the report may be used to assist in diagnosing the disorder and/or developing a treatment plan.
[00019] In some embodiments, the AI platform deploy one or more algorithm selected from Non-Linear Regression, Clustering, Hierarchical Clustering –Agglomerative, Hierarchical Clustering –Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization)Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF)and combination thereof.

Brief Description of the Drawings
[00020] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00021] FIG. 1 is a flowchart illustrating a method for assessing an individual's psychological state, according to some embodiments of the present disclosure.
[00022] FIG. 2 is a block diagram illustrating a system, according to some embodiments of the present disclosure.

Detailed Description
[00023] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00024] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00025] Following are more detailed descriptions of various concepts related to, and implementations of, methods, apparatuses, and systems of present disclosure. The various concepts introduced above and discussed in greater detail below may be implemented in any of numerous ways.
[00026] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00027] The present invention relates generally to bio-signal collection methods, more particularly to a system and method for evaluating clinical response data based psychological performance analysis by using machine learning.
[00028] FIG. 1 is a flowchart that describes a method for assessing an individual's psychological state, according to some embodiments of the present disclosure. In some embodiments, at 110, the method may include obtaining brain function data from the individual using one or more brain imaging techniques. At 120, the method may include analysing, through an AI platform, the brain function data to determine one or more features indicative of the individual's psychological state. At 130, the method may include comparing the one or more features to one or more reference values corresponding to the individual's psychological state. At 140, the method may include generating a report indicating the individual's psychological state based on the comparison.
[00029] In some embodiments, the brain function data may comprise electroencephalography (EEG)data, functional magnetic resonance imaging (fMRI)data, or a combination thereof. In some embodiments, the AI platform deploy machine learning based algorithm selected from k-nearest neighbour (kNN) algorithm, a Naïve Bayes algorithm, an algorithm employing decision trees, a linear regression algorithm, a support vector machine (SVM)and/or neural networks. In some embodiments, the report may be used to assist in diagnosing the disorder and/or developing a treatment plan.
[00030] FIG. 2 is a block diagram that describes a system 200, according to some embodiments of the present disclosure. In some embodiments, the system 200 may include one or more brain imaging devices 210 for obtaining brain function data from the individual and a display device 230 for displaying the report. The system 200 may also include a processor 220 configured to analyse the brain function data using an AI platform to determine one or more features indicative of the individual's psychological state, compare the one or more features to one or more reference values corresponding to the individual's psychological state, and generate a report indicating the individual's psychological state based on the comparison.
[00031] In some embodiments, the report indicating the individual's psychological state may be based on the one or more features and the psychological test results. In some embodiments, the individual may be a patient suffering from a psychiatric or neurological disorder. The report may be used to assist in diagnosing the disorder and/or developing a treatment plan. In some embodiments, the AI platform deploy one or more algorithm selected from Non-Linear Regression, Clustering, Hierarchical Clustering –Agglomerative, Hierarchical Clustering –Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization)Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF)and combination thereof.
[00032] The present disclosure may include a method for assessing the psychological state of an individual based on data pertaining to the individual's brain function. This method may include techniques for obtaining brain function data from the individual, such as one or more imaging techniques for the brain. Analyzing the data from the individual's brain activity using an AI platform in order to identify one or more characteristics that are indicative of the individual's psychological condition is another possible embodiment. Comparing the one or more characteristics to one or more reference values that correlate to the individual's psychological state is another possibility that may be included in embodiments. In certain embodiments, there is also the possibility of producing a report that, on the basis of the comparison, indicates the individual's psychological condition.
[00033] Electroencephalography (EEG) data, functional magnetic resonance imaging (fMRI) data, or a mix of the two may be included as part of the data pertaining to brain function in some implementations. In certain implementations, the artificial intelligence platform makes use of a machine learning-based algorithm chosen from among the k-nearest neighbour (kNN) algorithm, a Naive Bayes algorithm, an algorithm utilising decision trees, a linear regression algorithm, a support vector machine (SVM), and/or neural networks. In some implementations, the report may be used to help in the process of identifying the disease and/or coming up with a treatment strategy.
[00034] A system for evaluating an individual's psychological state on the basis of data derived from their brain function may also be included in some embodiments of the present disclosure. This system may include one or more brain imaging devices for the purpose of collecting brain function data from the individual. A processor that is configured to analyse the brain function data using an artificial intelligence platform to determine one or more features indicative of the individual's psychological state, compare the one or more features to one or more reference values corresponding to the individual's psychological state, and generate a report indicating the individual's psychological state based on the comparison may also be included in some embodiments. Embodiments may also include a memory device that is configured to store the results of the analysis of the brain function data. Display devices, for the purpose of showing the report, may also be included in embodiments.
[00035] The report that indicates the individual's psychological condition may be derived from the individual's results on the psychological test and one or more of the attributes, according to various implementations. In some implementations, the person may take the form of a patient who is afflicted with a psychological or neurological condition. In some implementations, the report may be used to help in the process of identifying the disease and/or coming up with a treatment strategy.
[00036] In certain implementations, the AI platform will deploy one or more algorithms chosen from the following categories: Non-Linear Regression, Clustering, Hierarchical Clustering –Agglomerative, Hierarchical Clustering –Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization)Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Fact.
[00037] Evaluating clinical response data based on psychological performance analysis is a complex process that requires the use of sophisticated tools and techniques. Machine learning is one such tool that can be used to analyze and interpret large volumes of clinical response data. This technical writing aims to provide a detailed overview of how machine learning can be used for evaluating clinical response data based on psychological performance analysis.
[00038] Machine learning can be used to evaluate clinical response data by analyzing psychological performance data. Psychological performance data can be collected through a variety of tests, including cognitive tests, behavioral assessments, and self-report surveys. These tests can be used to assess a patient's mental health, cognitive functioning, and emotional well-being.
[00039] Once psychological performance data has been collected, it can be analyzed using machine learning algorithms to identify patterns and relationships between different variables. For example, machine learning algorithms can be used to identify correlations between certain psychological traits and clinical outcomes, such as treatment response rates or symptom improvement.
[00040] Machine learning algorithms can also be used to develop predictive models for clinical response data. These models can be used to identify patients who are at risk for poor treatment outcomes or to identify the most effective treatment strategies for individual patients.
[00041] Machine learning is a powerful tool for evaluating clinical response data based on psychological performance analysis. By analyzing large volumes of data, machine learning algorithms can identify patterns and relationships that are not easily discernible through traditional statistical analysis. This can lead to more accurate predictions and better treatment outcomes for patients. However, it is important to ensure that the data used in machine learning algorithms is of high quality and that the algorithms are validated before being used in clinical settings.
[00042] Variations of those preferred embodiments may become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect skilled artisans to employ such variations as appropriate, and the inventors intend for the invention to be practiced otherwise than as specifically described herein. Accordingly, this invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context.
[00043] Processing device may be provided by one or more processors such as a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00044] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00045] All references to a/an/the element, apparatus, component, means, step, etc. are to be interpreted as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated. The discussion above and below in respect of any of the aspects of the present disclosure is also in applicable parts relevant to any other aspect of the present disclosure.
[00046] The wordings such as include, including, comprise and comprising do not exclude elements or steps which are present but not listed in the description and the claims.
[00047] It also shall be noted that as used herein and in the appended claims, the singular forms a, an, and the include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
[00048] Various techniques may be described herein in the general context of software, hardware elements, or program modules. Generally, such modules include routines, programs, objects, elements, components, data structures, and so forth that perform particular tasks or implement particular abstract data types. The terms module, functionality, and component as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that the techniques may be implemented on a variety of commercial computing platforms having a variety of processors.

Claims
I/We Claim:
1. A method for assessing an individual's psychological state based on brain function data, comprising:
a) obtaining brain function data from said individual using one or more brain imaging techniques;
b) analysing, through an AI platform, said brain function data to determine one or more features indicative of said individual's psychological state;
c) comparing said one or more features to one or more reference values corresponding to said individual's psychological state; and
d) generating a report indicating said individual's psychological state based on said comparison.
2. The method of claim 1, wherein said brain function data comprises electroencephalography (EEG) data, functional magnetic resonance imaging (fMRI) data, or a combination thereof.
3. The method of claim 1, wherein the AI platform deploy machine learning based algorithm selected from k-nearest neighbour (kNN) algorithm, a Naïve Bayes algorithm, an algorithm employing decision trees, a linear regression algorithm, a support vector machine (SVM) and/or neural networks.

4. The method of claim 1, wherein said report is used to assist in diagnosing said disorder and/or developing a treatment plan.
5. A system for assessing an individual's psychological state based on brain function data, comprising:
a) one or more brain imaging devices for obtaining brain function data from said individual;
b) a processor configured to analyse said brain function data using an AI platform to determine one or more features indicative of said individual's psychological state, compare said one or more features to one or more reference values corresponding to said individual's psychological state, and generate a report indicating said individual's psychological state based on said comparison; and
c) a display device for displaying said report.
6. The system of claim 5, wherein said report indicating said individual's psychological state is based on said one or more features and said psychological test results.
7. The system of claim 6, wherein said individual is a patient suffering from a psychiatric or neurological disorder, and wherein said report is used to assist in diagnosing said disorder and/or developing a treatment plan.
8. The system of claim 5, wherein the AI platform deploy one or more algorithm selected from Non-Linear Regression, Clustering, Hierarchical Clustering – Agglomerative, Hierarchical Clustering – Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization) Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF) and combination thereof.

 

CLINICAL RESPONSE DATA BASED PATIENT'S PSYCHOLOGICAL PERFORMANCE ANALYSIS
Abstract
The present disclosure may include a method for assessing the psychological state of an individual based on data pertaining to the individual's brain function. This method may include techniques for obtaining brain function data from the individual, such as one or more imaging techniques for the brain. Analyzing the data from the individual's brain activity using an AI platform in order to identify one or more characteristics that are indicative of the individual's psychological condition. In certain embodiments, there is also the possibility of producing a report that, on the basis of the comparison, indicates the individual's psychological condition. , Claims:Claims
I/We Claim:
1. A method for assessing an individual's psychological state based on brain function data, comprising:
a) obtaining brain function data from said individual using one or more brain imaging techniques;
b) analysing, through an AI platform, said brain function data to determine one or more features indicative of said individual's psychological state;
c) comparing said one or more features to one or more reference values corresponding to said individual's psychological state; and
d) generating a report indicating said individual's psychological state based on said comparison.
2. The method of claim 1, wherein said brain function data comprises electroencephalography (EEG) data, functional magnetic resonance imaging (fMRI) data, or a combination thereof.
3. The method of claim 1, wherein the AI platform deploy machine learning based algorithm selected from k-nearest neighbour (kNN) algorithm, a Naïve Bayes algorithm, an algorithm employing decision trees, a linear regression algorithm, a support vector machine (SVM) and/or neural networks.

4. The method of claim 1, wherein said report is used to assist in diagnosing said disorder and/or developing a treatment plan.
5. A system for assessing an individual's psychological state based on brain function data, comprising:
a) one or more brain imaging devices for obtaining brain function data from said individual;
b) a processor configured to analyse said brain function data using an AI platform to determine one or more features indicative of said individual's psychological state, compare said one or more features to one or more reference values corresponding to said individual's psychological state, and generate a report indicating said individual's psychological state based on said comparison; and
c) a display device for displaying said report.
6. The system of claim 5, wherein said report indicating said individual's psychological state is based on said one or more features and said psychological test results.
7. The system of claim 6, wherein said individual is a patient suffering from a psychiatric or neurological disorder, and wherein said report is used to assist in diagnosing said disorder and/or developing a treatment plan.
8. The system of claim 5, wherein the AI platform deploy one or more algorithm selected from Non-Linear Regression, Clustering, Hierarchical Clustering – Agglomerative, Hierarchical Clustering – Divisive, K-Means Clustering, K-Nearest Neighbours Clustering, EM (Expectation Maximization) Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF) and combination thereof.