This document provides an overview of clustering in machine learning. It discusses what clustering is, the different types of clustering including centroid-based, density-based, distribution-based, hierarchical, and grid-based clustering. It also provides examples of k-means clustering and discusses applications of clustering such as image recognition, biological research, and crime analysis.
Step by step operations by which we make a group of objects in which attributes
of all the objects are nearly similar, known as clustering. So, a cluster is a collection of
objects that acquire nearly same attribute values. The property of an object in a cluster is
similar to other objects in same cluster but different with objects of other clusters.
Clustering is used in wide range of applications like pattern recognition, image processing,
data analysis, machine learning etc. Nowadays, more attention has been put on categorical
data rather than numerical data. Where, the range of numerical attributes organizes in a
class like small, medium, high, and so on. There is wide range of algorithm that used to
make clusters of given categorical data. Our approach is to enhance the working on well-
known clustering algorithm k-modes to improve accuracy of algorithm. We proposed a new
approach named “High Accuracy Clustering Algorithm for Categorical datasets”.
Literature Survey: Clustering TechniqueEditor IJCATR
Clustering is a partition of data into the groups of similar or dissimilar objects. Clustering is unsupervised learning
technique helps to find out hidden patterns of Data Objects. These hidden patterns represent a data concept. Clustering is used in many
data mining applications for data analysis by finding data patterns. There is a number of clustering techniques and algorithms are
available to cluster the data object. According to the type of data object and structure appropriate clustering technique is selected. This
survey focuses on the clustering techniques for their input attribute data type, their input parameters and output. The main objective is
not to understand the actual working of clustering technique. Instead, the input data requirement and input parameters of clustering
technique are focused.
Clustering is the process of making a group of abstract objects into classes of similar objects. Clustering helps to splits data into several subsets. Each of these subsets contains data similar to each other, and these subsets are called clusters. Now that the data from our customer base is divided into clusters, we can make an informed decision about who we think is best suited for this product.
Cluster analysis is a data analysis technique that explores the naturally occurring groups within a data set known as clusters. Cluster analysis doesn't need to group data points into any predefined groups, which means that it is an unsupervised learning method.
Cluster analysis is a data analysis technique that explores the naturally occurring groups within a data set known as clusters. Cluster analysis doesn't need to group data points into any predefined groups, which means that it is an unsupervised learning method.
Step by step operations by which we make a group of objects in which attributes
of all the objects are nearly similar, known as clustering. So, a cluster is a collection of
objects that acquire nearly same attribute values. The property of an object in a cluster is
similar to other objects in same cluster but different with objects of other clusters.
Clustering is used in wide range of applications like pattern recognition, image processing,
data analysis, machine learning etc. Nowadays, more attention has been put on categorical
data rather than numerical data. Where, the range of numerical attributes organizes in a
class like small, medium, high, and so on. There is wide range of algorithm that used to
make clusters of given categorical data. Our approach is to enhance the working on well-
known clustering algorithm k-modes to improve accuracy of algorithm. We proposed a new
approach named “High Accuracy Clustering Algorithm for Categorical datasets”.
Literature Survey: Clustering TechniqueEditor IJCATR
Clustering is a partition of data into the groups of similar or dissimilar objects. Clustering is unsupervised learning
technique helps to find out hidden patterns of Data Objects. These hidden patterns represent a data concept. Clustering is used in many
data mining applications for data analysis by finding data patterns. There is a number of clustering techniques and algorithms are
available to cluster the data object. According to the type of data object and structure appropriate clustering technique is selected. This
survey focuses on the clustering techniques for their input attribute data type, their input parameters and output. The main objective is
not to understand the actual working of clustering technique. Instead, the input data requirement and input parameters of clustering
technique are focused.
Clustering is the process of making a group of abstract objects into classes of similar objects. Clustering helps to splits data into several subsets. Each of these subsets contains data similar to each other, and these subsets are called clusters. Now that the data from our customer base is divided into clusters, we can make an informed decision about who we think is best suited for this product.
Cluster analysis is a data analysis technique that explores the naturally occurring groups within a data set known as clusters. Cluster analysis doesn't need to group data points into any predefined groups, which means that it is an unsupervised learning method.
Cluster analysis is a data analysis technique that explores the naturally occurring groups within a data set known as clusters. Cluster analysis doesn't need to group data points into any predefined groups, which means that it is an unsupervised learning method.
This article got published in the Software Developer's Journal's February Edition.
It describes the use of MapReduce paradigm to design Clustering algorithms and explain three algorithms using MapReduce.
- K-Means Clustering
- Canopy Clustering
- MinHash Clustering
Here, in this paper we are introducing a dynamic clustering algorithm using fuzzy c-mean clustering algorithm. We will try to process several sets patterns together to find a common structure. The structure is finalized by interchanging prototypes of the given data and by moving the prototypes of the subsequent clusters toward each other. In regular FCM clustering algorithm, fixed numbers of clusters are chosen and those are pre-defined. If, in case, the number of chosen clusters is wrong, then the final result will degrade the purity of the cluster. In our proposed algorithm this drawback will be overcome by using dynamic clustering architecture. Here we will take fixed number of clusters in the beginning but on iterations the algorithm will increase the number of clusters automatically depending on the nature and type of data, which will increase the purity of the result at the end. A detailed clustering algorithm is developed on a basis of the standard FCM method and will be illustrated by means of numeric examples.
Comparison Between Clustering Algorithms for Microarray Data AnalysisIOSR Journals
Currently, there are two techniques used for large-scale gene-expression profiling; microarray and
RNA-Sequence (RNA-Seq).This paper is intended to study and compare different clustering algorithms that used
in microarray data analysis. Microarray is a DNA molecules array which allows multiple hybridization
experiments to be carried out simultaneously and trace expression levels of thousands of genes. It is a highthroughput
technology for gene expression analysis and becomes an effective tool for biomedical research.
Microarray analysis aims to interpret the data produced from experiments on DNA, RNA, and protein
microarrays, which enable researchers to investigate the expression state of a large number of genes. Data
clustering represents the first and main process in microarray data analysis. The k-means, fuzzy c-mean, selforganizing
map, and hierarchical clustering algorithms are under investigation in this paper. These algorithms
are compared based on their clustering model.
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail.
Below topics are explained in this Machine Learning tutorial part -2 :
1. Clustering
- What is clustering?
- K-Means clustering
- Flowchart to understand K-Means clustering
- Demo - Clustering of cars based on brands
2. Logistic regression
- What is logistic regression?
- Logistic regression curve & Sigmoid function
- Demo - Classify a tumor as malignant or benign based on features
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
AI Professionals use top machine learning algorithms to automate models that analyze more extensive and complex data which was not possible in older machine learning algos.
k-Means is a rather simple but well known algorithms for grouping objects, clustering. Again all objects need to be represented as a set of numerical features. In addition the user has to specify the number of groups (referred to as k) he wishes to identify. Each object can be thought of as being represented by some feature vector in an n dimensional space, n being the number of all features used to describe the objects to cluster. The algorithm then randomly chooses k points in that vector space, these point serve as the initial centers of the clusters. Afterwards all objects are each assigned to center they are closest to. Usually the distance measure is chosen by the user and determined by the learning task. After that, for each cluster a new center is computed by averaging the feature vectors of all objects assigned to it. The process of assigning objects and recomputing centers is repeated until the process converges. The algorithm can be proven to converge after a finite number of iterations. Several tweaks concerning distance measure, initial center choice and computation of new average centers have been explored, as well as the estimation of the number of clusters k. Yet the main principle always remains the same. In this project we will discuss about K-means clustering algorithm, implementation and its application to the problem of unsupervised learning
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
This article got published in the Software Developer's Journal's February Edition.
It describes the use of MapReduce paradigm to design Clustering algorithms and explain three algorithms using MapReduce.
- K-Means Clustering
- Canopy Clustering
- MinHash Clustering
Here, in this paper we are introducing a dynamic clustering algorithm using fuzzy c-mean clustering algorithm. We will try to process several sets patterns together to find a common structure. The structure is finalized by interchanging prototypes of the given data and by moving the prototypes of the subsequent clusters toward each other. In regular FCM clustering algorithm, fixed numbers of clusters are chosen and those are pre-defined. If, in case, the number of chosen clusters is wrong, then the final result will degrade the purity of the cluster. In our proposed algorithm this drawback will be overcome by using dynamic clustering architecture. Here we will take fixed number of clusters in the beginning but on iterations the algorithm will increase the number of clusters automatically depending on the nature and type of data, which will increase the purity of the result at the end. A detailed clustering algorithm is developed on a basis of the standard FCM method and will be illustrated by means of numeric examples.
Comparison Between Clustering Algorithms for Microarray Data AnalysisIOSR Journals
Currently, there are two techniques used for large-scale gene-expression profiling; microarray and
RNA-Sequence (RNA-Seq).This paper is intended to study and compare different clustering algorithms that used
in microarray data analysis. Microarray is a DNA molecules array which allows multiple hybridization
experiments to be carried out simultaneously and trace expression levels of thousands of genes. It is a highthroughput
technology for gene expression analysis and becomes an effective tool for biomedical research.
Microarray analysis aims to interpret the data produced from experiments on DNA, RNA, and protein
microarrays, which enable researchers to investigate the expression state of a large number of genes. Data
clustering represents the first and main process in microarray data analysis. The k-means, fuzzy c-mean, selforganizing
map, and hierarchical clustering algorithms are under investigation in this paper. These algorithms
are compared based on their clustering model.
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail.
Below topics are explained in this Machine Learning tutorial part -2 :
1. Clustering
- What is clustering?
- K-Means clustering
- Flowchart to understand K-Means clustering
- Demo - Clustering of cars based on brands
2. Logistic regression
- What is logistic regression?
- Logistic regression curve & Sigmoid function
- Demo - Classify a tumor as malignant or benign based on features
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
AI Professionals use top machine learning algorithms to automate models that analyze more extensive and complex data which was not possible in older machine learning algos.
k-Means is a rather simple but well known algorithms for grouping objects, clustering. Again all objects need to be represented as a set of numerical features. In addition the user has to specify the number of groups (referred to as k) he wishes to identify. Each object can be thought of as being represented by some feature vector in an n dimensional space, n being the number of all features used to describe the objects to cluster. The algorithm then randomly chooses k points in that vector space, these point serve as the initial centers of the clusters. Afterwards all objects are each assigned to center they are closest to. Usually the distance measure is chosen by the user and determined by the learning task. After that, for each cluster a new center is computed by averaging the feature vectors of all objects assigned to it. The process of assigning objects and recomputing centers is repeated until the process converges. The algorithm can be proven to converge after a finite number of iterations. Several tweaks concerning distance measure, initial center choice and computation of new average centers have been explored, as well as the estimation of the number of clusters k. Yet the main principle always remains the same. In this project we will discuss about K-means clustering algorithm, implementation and its application to the problem of unsupervised learning
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
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This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
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Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
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After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Elevating Tactical DDD Patterns Through Object Calisthenics
Clustering in Machine Learning.pdf
1. Clustering in Machine Learning
In this article, we will study an unsupervised learning-based technique known
as clustering in machine learning. Here, we will discuss what clustering really
is, What are its types? We will look at the algorithms involved in the clustering
technique.
Clustering in Machine Learning
Let’s try to understand what clustering exactly is. Examples make the job a lot
more easier.
So, as we know, there are two types of learning: active and passive.
Passive means that the model follows a certain pre-written path and is also
done under supervision. This doesn’t mean something like we will take
unsupervised learning on the active side. It is just that the human intervention
in unsupervised learning is quite minimal as compared to supervised learning.
Also, we have unlabeled data in unsupervised learning. So, here, the algorithm
has to completely analyze the data, find patterns, and cluster the data
depicting similar features.
For example, if we provide a dataset consisting of images of two different
objects. The model will scan the images for certain features. If some images
have matching features, it will form a cluster.
2. Note:-Active learning is a different concept. It’s applicable for semi-supervised
and reinforcement learning techniques.
Examples of Clustering in Machine Learning
A real-life example would be: -Trying to solve a hard problem in chess. The
possibilities to checkmate the king are endless. There is no predefined or
pre-set solution in chess. You have to analyze the positions, your pieces, the
opponent’s pieces and find a solution.
These traits to find the solution are the dataset, you are the model that has to
analyze and find the answer. This is what unsupervised learning is.
Now let’s understand clustering. In clustering, we classify data points into
clusters based on similar features rather than labels. The labelling part in
clustering comes at the end when clustering is over.
Also, we should add a lot of data to the dataset, to increase the accuracy of the
results. The algorithm will learn various patterns in the dataset.
Like, it will look for certain traits and features and compare the similarities
between the data.
There is a difference here between classification and clustering. The labeling
part involves a lot of human intervention that proves both costly and
time-consuming. Although labelling gets fairly simple in unsupervised
learning, the model would have to process more due to more analysis.
Let’s take an example. If we have a dataset consisting of images of tigers,
zebras, leopards, cheetahs.
3. The clustering algorithm would analyze this dataset and then divide the data
based on some specific characteristics. The characteristics would include fur
color, patterns (spots, stripes), face shape, etc.
The model would remember the pattern in which it classified the data. This
knowledge will come in handy for future unknown data.
We also have other applications of clustering like fake-news detection, fraud
detection, spam mail segregation, etc. Now, let’s dive a bit deeper into
clustering now that we have seen and understood it.
There are various types of clustering that we should know about. These will
help us to further classify and understand the various algorithms that
unsupervised learning has.
These include:
● Centroid-based clustering
● Density-based clustering
● Distribution-based clustering
● Hierarchical clustering
● Grid clustering
Now let’s understand these one-by-one.
Types of Clustering in Machine Learning
1. Centroid-Based Clustering in Machine Learning
In centroid-based clustering, we form clusters around several points that act
as the centroids. The k-means clustering algorithm is the perfect example of
4. the Centroid-based clustering method. Here, we form k number of clusters
that have k number of centroids.
The algorithm assigns the datapoints to certain cluster centres (centroids)
based on their proximity to certain centroids.
The algorithm measures the Euclidean distances between the datapoints and
all k centroids. The one that is the nearest will get clustered with that
particular centroid.
Also, k-means is the most widely used centroid-based clustering algorithm.
The primary aim of the algorithm is to simplify an N-dimensional dataset into
smaller K clusters.
K-means Clustering in Machine Learning
Let’s try to understand more about k-means clustering. It is an iterative
clustering type of algorithm. This means that it compares each datapoint’s
proximity with the centroids one-by-one in an iterative fashion. The premise
of this algorithm is that it has to find the maximum (local maxima) or the best
possible value for each iteration.
Let’s understand the working of the algorithm with the help of some images:
Let’s say we have two different types of datapoints. Red colour and blue
colour.
5. Now, for the next step, let’s assign the value of k. Let’s say k=2 as we have two
types of data. K=2 means we will have two centroids far from each other
Note:- The centroids should always remain distant from each other to avoid
any confusion and error.
6. The two-colored crosses are the centroids.
Now the algorithm will compare the distance of each point with the centroids.
The points will join the cluster whose centroid is nearer to them. This
algorithm makes nice use of the distance metrics for this purpose. The points
are now assigned to their respective clusters.
7. After this, again centroids are assigned for the clusters and again they
re-adjust themselves.
The last two steps will keep on continuing until the algorithm achieves
maximum accuracy (maxima) or you can say, until the clusters stop moving
and everything becomes stable.
So, let’s see the coding part of it:
Step 1: First, we will import all the necessary libraries. We have the basic
import libraries:
8. from sklearn.datasets import make_classification
from sklearn.cluster import KMeans
from matplotlib import pyplot
from numpy import unique
from numpy import where
Here, make_classification is for the dataset. KMeans is to import the model
for the KMeans algorithm.
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If there are any special or unique elements in the NumPy array, we can extract
them with the help of the unique function provided by the unique library.
Also, in the case of the where function, you can relate it with the conditional
operator in mathematics.
You have to give a condition to the where function in order to get the output
according to it. Remember that these two functions belong to the Numpy
group.
Here, we have used sklearn.datasets in order to include more parameters
related to the datasets.
Step-2: Let’s try to define the dataset. We will use the make_classification
function to define our dataset and to include certain parameters with it.
A, _ = make_classification(n_samples=1000, n_features=4,
n_informative=2, n_redundant=0, n_repeated=0,
n_clusters_per_class=1, random_state=4)
9. Remember that, whatever parameter you choose to have, the output would
look different for each case.
Also, these parameters have specific meanings.
n_samples is the number of samples.
n_features is the number of features that you would want to have in the
dataset.
Now, these next three are a bit important to know. As they are responsible for
how your data would look.
n_informative tells the number of important and informative features of your
data.
n_redundant gives you random combinations of informative features.
n_repeated helps to draw out the duplicate features from the above two
features.
The Numpy array stores the features in the same order as mentioned and
Random_state helps in giving random numbers in the same order.
Step-3: Now, we will bring in our model. In the parameter of our model or the
function name (KMeans), we will give the number of clusters we need:
model = KMeans(n_clusters=3)
Step-4: Now, here we run the model. We try to fit in the data that we have
defined above.
10. model.fit(A)
After execution, it will also show a certain output message (depends on the
framework that you use)
Step-5: The function predict() will help to analyze and predict the dataset to
make clusters.
B = model.predict(A)
Step-6: Here the unique function comes into play. This will help to segregate
certain features for the cluster in a NumPy array.
clust = unique(B)
Step-7: Now, we will run the entire system in a loop so as to keep taking the
next set of data for creating the clusters. Here, the clust variable already has
the segregated data for the cluster.
We will use it in the where function and create a condition.
In the end, we will see a graph plotted as a result (because pyplot and
matplotlib come into play here).
for cluster in clust:
11. C = where(B == cluster)
pyplot.scatter(A[C, 0], A[C, 1])
pyplot.show()
2. Density-Based Clustering in Machine Learning
In this type of clustering, the clustering doesn’t happen around centroid or
central points, but the cluster forms where the density looks higher. Also, the
advantage is that unlike centroid-based clustering it doesn’t include all the
points.
Including all the points can create unnecessary noise in the data and that is
where density-based clustering has an edge over centroid-based clustering.
The sparse/noise datapoints are included in order to define the border
between clusters. These points are always outside the main cluster.
Also, one negative thing is that in density-based clustering, the cluster borders
are defined by decreasing density. So, the borders might be of varied shapes
12. that make it difficult to draw a perfect border for clusters. However, there are
many methods out there to improve these kinds of problems, but we look at
them some other time.
We have three main algorithms in Density-based clustering – DBSCAN,
HDBSCAN, and OPTICS.
DBSCAN uses a fixed distance for separating the dense clusters from the noise
datapoints. It is the fastest among clustering algorithms.
HDBSCAN uses a range of distances to separate itself from the noise. It
requires the least amount of user-input.
OPTICS measures the distance between neighboring features and it draws a
reachability plot to separate itself from the noise datapoints.
Now, we will use the same code but some different functions to understand
density-based clustering. So, we will only mention the different functions here
as the rest is the same.
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So, let’s have a look at DBSCAN: We just need to import DBSCAN:
from sklearn.cluster import DBSCAN
This will import the DBSCAN model into your program. Now to define your
model and to provide it with the arguments:
model = DBSCAN(eps=0.20, min_samples=5)
13. Here, eps is the max. distance between two data points. This is the prime
parameter for the DBSCAN as the values we enter in eps would change the
plot every time.
The reason is that cluster density would look different for different values.
Also, min_samples helps to set the minimum number of samples we want
within a neighborhood collection of features. So, the output for the given value
set would be:
3. Distribution-Based Clustering in Machine
Learning
In distribution-based clustering, if the distance between the point and the
central distribution of points increases, then the probability of the point being
included in the distribution decreases.
For problems like these, we use the Gaussian distribution model. The model
works like, there will be a fixed number of distributors for the Gaussian model.
14. These distributors are concentric figures with decreasing color intensity from
inside to the outside.
The central part tends to be denser and it tends to decrease as we go outside as
we have bigger distributors now. So, even though distributors might contain
the same number of points, their densities may still differ due to the size of
distributors. Overfitting can be a bit of a problem for this type of clustering.
As long as we don’t set any strong criteria for points, we can avoid overfitting.
The Gaussian mixture model is generally used with the
expectation-maximization model. An important point to remember that we
cannot use this technique for density-based clustering as it would not properly
fit among the distributions.
Only expectation-maximization algorithms can work well with Gaussian
mixture models.
So let’s see how the Gaussian distribution looks in general:
15. The concentric figures may not be this perfect, but it’s a similar depiction of
what the real thing looks like.
Now, let’s understand the code involved in this:
The code has numerous similarities just like the previous case. So, we will only
mention the main components involved as the rest gets pretty much the same.
These are all easier examples, just to understand how the clustering works.
The coding has an enormous depth with numerous parameters for the
functions that we use. But, here we restrict to very few functions to make the
explanation simpler.
16. From sklearn.cluster import GaussianMixture
Firstly, we will import the model.
Note: All the models and algorithms that are a part of sklearn, we can import
them directly from sklearn and just use their function to define the models.
model = GaussianMixture(n_components=4)
Here, n_components is the number of mixture components ( the number of
different colored components that we will use in the plot).
So, the result would look like:
4. Hierarchical Clustering in Machine Learning
Well, in hierarchical clustering we deal with either merging of clusters or
division of a big cluster.
17. So, we should know that hierarchical clustering has two types: Agglomerative
hierarchical clustering and divisive hierarchical clustering.
In agglomerative clustering, we tend to merge smaller clusters into bigger
clusters. But this also has some process. If the two clusters that we compare
have similarities between them and if they are near to each other, then we
merge them.
In the case of divisive hierarchical clustering, we divide one big cluster into
n-smaller clusters. The clusters here are divided if some datapoints are not
similar to the larger cluster; we separate them and make an individual cluster
for them.
For solving the hierarchical clustering problem, we use the proximity
(distance) matrix in which we store the distances of each individual cluster
among each other.
For every merge or divide, the matrix would change because at every next step
we get a new cluster.
Like, if we have 5 clusters, we merge two of them, then the matrix will then
have 4 clusters and the distances stored in the matrix will eventually change
(Same case for divisive hierarchical clustering as well).
Also, for visualizing hierarchical clustering, we use a dendrogram, which is a
type of graph. In hierarchical clustering, the most important job is to calculate
the similarity between clusters.
18. It’s the most important part as it helps us to understand whether to merge or
divide the cluster. We have several methods like:
Single Linkage Algorithm (MIN)
● In this, we take two points from two individual clusters and these
points have to be the closest to each.
● We then calculate the distance and similarity between them.
● It works well for separating non-elliptical clusters.
● Noise in the data can create problems.
Complete Linkage Algorithm (MAX)
● This is the exact opposite of the MIN algorithm.
● We take the distance between the two farthest points in two clusters
and measure the distance and similarities.
● It works well even in the presence of noise.
● However, it can accidentally break bigger clusters as well.
Group Average
● Here, we take all the points and then measure them.
● It can accurately separate clusters even if there remains noise in
between clusters.
Distance Between the Centroids
● We can measure the distance between the centroids of both the
clusters.
● Although it’s not used that much as you have to find the centroid
after every merge or divide.
Ward’s Method
● This one is the last method to calculate the similarity.
19. ● Here, instead of taking the average, we take the sum of squares of
distances.
Note:- The complexity of space in hierarchical clustering is O(n2). We require
lots and lots of space to hold the matrix. For time complexity it is O(n3). The
iterations we perform can get complex if there is a large dataset.
We have some visual representations of how the clustering happens in
hierarchical clustering:
20.
21. There is also the proximity matrix and the dendrogram:
This is the proximity matrix. Now the dendrogram:
22. 5. Grid Clustering in Machine Learning
This technique is very useful for multidimensional spaces. Here, we can divide
the entire data space into a finite number of small cells. This helps a lot in
reducing the complexity of the problem and this is what separates grid
clustering from all conventional clustering.
We can recognize denser areas as places that have clusters. When we divide
the plane into cells, we can then calculate the density of each cell and then sort
them.
At first, we will select one cell and calculate its density. If it is more than the
normal density it will become a cluster. We will apply the same process for the
cell’s neighbors until there are no neighbors left. All the neighboring cells
would have grouped into a cluster. The process will continue until all the cells
are traversed.
Here, we focus on the cells rather than the data. This helps in reducing
complexity. The algorithms that fall under the grid-based clustering are the
STING and CLIQUE algorithms.
23. Applications of Clustering in Machine
Learning
So, finally, let’s have a look at the specific areas where this concept is applied.
Here are the top applications of the clustering concept:
● It’s useful in various image recognition platforms and also various
image segregation tasks. One such example is the biological field.
● Here, this can help the researchers to categorize and classify certain
unknown species.
● Clustering can come in handy for the city’s crime branch for
classifying different parts of a city based on the crime-rate.
● The algorithm can classify and tell based on the frequency and
number of cases that, which part has more criminal activities.
● It’s quite useful in data-mining as well because clustering can help
in understanding the data.
● It can be useful for banks as a fraud detection algorithm. The bank
sector faces the problem of credit card scams and frauds.
● Using clustering we can uncover several hidden patterns that may
tell us which scheme is a fraudulent scheme.
● We can understand various seismic zones based on the data and the
clustering algorithm can create clusters of various seismic activities
and active regions.
● In the health industry as well, we can use this algorithm to detect
various ailments, especially tumors and cancers.
● The search engine is a prime example of the clustering technique.
● It categorizes search results on the basis of billions of searches and
has the results ready on the go every time for every search.
● It can be of great use in analyzing data on soil for agriculture.
● With the data of the soil, it can classify whether the soil has the
necessary nutrient content for growing crops.
● It is also useful in the case of advertising the product based on
customer interest and feedback.
24. Conclusion
So, for this article, we have studied all the necessary aspects of clustering in
machine learning that a student or someone who wants to pursue ML should
know about.
We covered everything from what clustering is to what are its types. We even
have a few coding examples, more importantly, what function and library we
should use in ML is more important than to understand the whole code. You
can understand the whole code if you know Python language.
We even saw the algorithms that fall under various categories. Also, several
diagrams have been used for better understanding.