K-Means Clustering Algorithm.pptx
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The k-means clustering algorithm is an unsupervised machine learning algorithm that groups unlabeled data points into k number of clusters. It works by first selecting k random cluster centroids and then assigns each data point to its nearest centroid, forming k clusters. It then recalculates the positions of the centroids and reassigns data points in an iterative process until centroids stabilize. The optimal number of clusters k can be determined using the elbow method by plotting the within-cluster sum of squares against k and selecting the k value at the point of inflection of the curve, resembling an elbow.
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The document discusses various unsupervised learning techniques including clustering algorithms like k-means, k-medoids, hierarchical clustering and density-based clustering. It explains how k-means clustering works by selecting initial random centroids and iteratively reassigning data points to the closest centroid. The elbow method is described as a way to determine the optimal number of clusters k. The document also discusses how k-medoids clustering is more robust to outliers than k-means because it uses actual data points as cluster representatives rather than centroids.
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This document provides an overview of clustering and k-means clustering algorithms. It begins by defining clustering as the process of grouping similar objects together and dissimilar objects separately. K-means clustering is introduced as an algorithm that partitions data points into k clusters by minimizing total intra-cluster variance, iteratively updating cluster means. The k-means algorithm and an example are described in detail. Weaknesses and applications are discussed. Finally, vector quantization and principal component analysis are briefly introduced.
Unsupervised learning Algorithms and Assumptions
Topics :
Introduction to unsupervised learning
Unsupervised learning Algorithms and Assumptions
K-Means algorithm – introduction
Implementation of K-means algorithm
Hierarchical Clustering – need and importance of hierarchical clustering
Agglomerative Hierarchical Clustering
Working of dendrogram
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Gaussian Mixture Models – Introduction, importance and need of the model
Normal , Gaussian distribution
Implementation of Gaussian mixture model
Understand the different distance metrics used in clustering
Euclidean, Manhattan, Cosine, Mahala Nobis
Features of a Cluster – Labels, Centroids, Inertia, Eigen vectors and Eigen values
Principal component analysis
Supervised learning (classification)
Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations
New data is classified based on the training set
Unsupervised learning (clustering)
The class labels of training data is unknown
Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data
Types of Hierarchical Clustering
There are mainly two types of hierarchical clustering:
Agglomerative hierarchical clustering
Divisive Hierarchical clustering
A distribution in statistics is a function that shows the possible values for a variable and how often they occur.
In probability theory and statistics, the Normal Distribution, also called the Gaussian Distribution.
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The document provides an overview of clustering methods and algorithms. It defines clustering as the process of grouping objects that are similar to each other and dissimilar to objects in other groups. It discusses existing clustering methods like K-means, hierarchical clustering, and density-based clustering. For each method, it outlines the basic steps and provides an example application of K-means clustering to demonstrate how the algorithm works. The document also discusses evaluating clustering results and different measures used to assess cluster validity.
K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...
This K-Means clustering algorithm presentation will take you through the machine learning introduction, types of clustering algorithms, k-means clustering, how does K-Means clustering work and at least explains K-Means clustering by taking a real life use case. This Machine Learning algorithm tutorial video is ideal for beginners to learn how K-Means clustering work.
Below topics are covered in this K-Means Clustering Algorithm presentation:
1. Types of Machine Learning?
2. What is K-Means Clustering?
3. Applications of K-Means Clustering
4. Common distance measure
5. How does K-Means Clustering work?
6. K-Means Clustering Algorithm
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8. Use case: Color compression
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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.
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Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
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What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
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Slide-TIF311-DM-10-11.ppt
This document provides an overview of data mining techniques including clustering and classification. It defines clustering as the process of organizing objects into groups of similar objects. The document outlines several existing clustering methods such as hierarchical, partitioning, and probabilistic clustering. It also defines classification as assigning data to predefined categories or classes. Several classification examples are described along with techniques like decision trees, k-nearest neighbors, regression, and neural networks. The document concludes that these techniques are useful for simplifying data, detecting patterns, and performing supervised and unsupervised learning.
Slide-TIF311-DM-10-11.ppt
Clustering is an unsupervised learning technique used to group unlabeled data points into clusters based on similarity. There are several clustering methods including hierarchical, partitioning, density-based, and grid-based approaches. K-means clustering is a popular partitioning method that groups data into K number of clusters by minimizing distances between data points and cluster centers. It works by randomly selecting K data points as initial cluster centers and then iteratively reassigning all other points to clusters while updating the cluster centers until the clusters are stable.
K Means Clustering Algorithm | K Means Example in Python | Machine Learning A...
The document discusses the K-Means clustering algorithm. It begins by defining clustering as grouping similar data points together. It then describes K-Means clustering, which groups data into K number of clusters by minimizing distances between points and cluster centers. The K-Means algorithm works by randomly selecting K initial cluster centers, assigning each point to the closest center, and recalculating centers as points are assigned until clusters stabilize. The best number of K clusters is found through trial and error to minimize variation between points and clusters.
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This document discusses hierarchical clustering algorithms. It describes hierarchical clustering as a method that forms clusters based on a hierarchical (tree-like) structure, with new clusters being formed from previously established clusters. There are two main approaches: agglomerative, which is a bottom-up approach that treats each data point as an individual cluster initially, and divisive, which is a top-down approach that treats all data points as one cluster initially. The document provides examples of hierarchical clustering algorithms and discusses key aspects like linkage criteria and interpreting dendrograms.
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The document discusses various clustering techniques. It begins by introducing clustering and some common techniques, including partitioning algorithms like k-means, hierarchical algorithms, and density-based algorithms. It then focuses on explaining the k-means algorithm in detail, providing pseudocode and an example application with 16 data points. Key aspects of k-means discussed include initializing cluster centroids, calculating distances between data points and centroids, assigning points to clusters, updating centroids, and determining convergence. The document concludes by analyzing pros and cons of k-means, such as the need to specify the number of clusters k and properly select initial centroids to avoid local optima.
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hierarchical clustering.pptx
- Hierarchical clustering is an unsupervised machine learning algorithm that groups unlabeled datasets into a hierarchical structure of clusters. It does not require predetermining the number of clusters as in K-means clustering.
- There are two approaches: agglomerative (bottom-up) and divisive (top-down). Agglomerative clustering starts with each data point as a cluster and merges them iteratively, while divisive clustering splits clusters iteratively.
- Hierarchical clustering represents the clustering hierarchy as a dendrogram tree structure. The optimal number of clusters can be determined by cutting the dendrogram tree at an appropriate level.
Training machine learning k means 2017
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K means Clustering - algorithm to cluster n objects
The k-means algorithm is an algorithm to cluster n objects based on attributes into k partitions, where k < n.
It is similar to the expectation-maximization algorithm for mixtures of Gaussians in that they both attempt to find the centers of natural clusters in the data.
It assumes that the object attributes form a vector space.
kmean clustering
K-means clustering groups data points into k clusters by minimizing the distance between points and cluster centroids. It works by randomly assigning points to initial centroids and then iteratively reassigning points to centroids until clusters are stable. Hierarchical clustering builds a dendrogram showing the relationship between clusters by either recursively merging or splitting clusters. Both are unsupervised learning techniques that group similar data points together without labels.
MODULE 4_ CLUSTERING.pptx
Clustering is an unsupervised learning technique used to group unlabeled data points into clusters based on similarity. It is widely used in data mining applications. The k-means algorithm is one of the simplest clustering algorithms that partitions data into k predefined clusters, where each data point belongs to the cluster with the nearest mean. It works by assigning data points to their closest cluster centroid and recalculating the centroids until clusters stabilize. The k-medoids algorithm is similar but uses actual data points as centroids instead of means, making it more robust to outliers.
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The KMeans Clustering algorithm is a process by which objects are classified into number of groups so that they are as much dissimilar as possible from one group to another, and as much similar as possible within each group. This algorithm is very useful in identifying patterns within groups and understanding the common characteristics to support decisions regarding pricing, product features, risk within certain groups, etc.Similar to K-Means Clustering Algorithm.pptx (20)
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06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
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apache nifi
apache pulsar
apache kafka
apache flink
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【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
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https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
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Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
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【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Analysis insight about a Flyball dog competition team's performance
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Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
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Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
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Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
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We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
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Intelligence supported media monitoring in veterinary medicine
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K-Means Clustering Algorithm.pptx
- 2. What is K-Means Algorithm? • K-Means Clustering is an unsupervised learning algorithm that is used to solve the clustering problems in machine learning or data science. • K-Means Clustering is an Unsupervised Learning algorithm, which groups the unlabeled dataset into different clusters. • Here K defines the number of pre-defined clusters that need to be created in the process, as if K=2, there will be two clusters, and for K=3, there will be three clusters, and so on.
- 3. • It allows us to cluster the data into different groups and a convenient way to discover the categories of groups in the unlabeled dataset on its own without the need for any training. • It is a centroid-based algorithm, where each cluster is associated with a centroid. The main aim of this algorithm is to minimize the sum of distances between the data point and their corresponding clusters. • The algorithm takes the unlabeled dataset as input, divides the dataset into k-number of clusters, and repeats the process until it does not find the best clusters. The value of k should be predetermined in this algorithm.
- 4. The k-means clustering algorithm mainly performs two tasks: • Determines the best value for K center points or centroids by an iterative process. • Assigns each data point to its closest k-center. Those data points which are near to the particular k-center, create a cluster. • Hence each cluster has datapoints with some commonalities, and it is away from other clusters.
- 5. • The below diagram explains the working of the K-means Clustering Algorithm:
- 6. How does the K-Means Algorithm Work? • Step-1: Select the number K to decide the number of clusters. • Step-2: Select random K points or centroids. (It can be other from the input dataset). • Step-3: Assign each data point to their closest centroid, which will form the predefined K clusters. • Step-4: Calculate the variance and place a new centroid of each cluster. • Step-5: Repeat the third steps, which means reassign each datapoint to the new closest centroid of each cluster. • Step-6: If any reassignment occurs, then go to step-4 else go to FINISH. • Step-7: The model is ready.
- 7. • Suppose we have two variables M1 and M2. The x-y axis scatter plot of these two variables is given below:
- 8. • Let's take number k of clusters, i.e., K=2, to identify the dataset and to put them into different clusters. It means here we will try to group these datasets into two different clusters. • We need to choose some random k points or centroid to form the cluster. These points can be either the points from the dataset or any other point. So, here we are selecting the below two points as k points, which are not the part of our dataset. Consider the below image:
- 10. • Now we will assign each data point of the scatter plot to its closest K-point or centroid. We will compute it by applying some mathematics that we have studied to calculate the distance between two points. So, we will draw a median between both the centroids. Consider the below image:
- 12. • From the above image, it is clear that points left side of the line is near to the K1 or blue centroid, and points to the right of the line are close to the yellow centroid. Let's color them as blue and yellow for clear visualization.
- 13. • As we need to find the closest cluster, so we will repeat the process by choosing a new centroid. To choose the new centroids, we will compute the center of gravity of these centroids, and will find new centroids as below:
- 15. • Next, we will reassign each datapoint to the new centroid. For this, we will repeat the same process of finding a median line. The median will be like below image:
- 16. • From the above image, we can see, one yellow point is on the left side of the line, and two blue points are right to the line. So, these three points will be assigned to new centroids. •
- 17. • As reassignment has taken place, so we will again go to the step-4, which is finding new centroids or K-points. • We will repeat the process by finding the center of gravity of centroids, so the new centroids will be as shown in the below image:
- 18. • As we got the new centroids so again will draw the median line and reassign the data points. So, the image will be:
- 19. • We can see in the above image; there are no dissimilar data points on either side of the line, which means our model is formed. Consider the below image:
- 20. • As our model is ready, so we can now remove the assumed centroids, and the two final clusters will be as shown in the below image:
- 21. How to choose the value of "K number of clusters" in K-means Clustering? • The performance of the K-means clustering algorithm depends upon highly efficient clusters that it forms. • But choosing the optimal number of clusters is a big task. There are some different ways to find the optimal number of clusters, but here we are discussing the most appropriate method to find the number of clusters or value of K. The method is given below:
- 22. Elbow Method • The Elbow method is one of the most popular ways to find the optimal number of clusters. This method uses the concept of WCSS value. • WCSS stands for Within Cluster Sum of Squares, which defines the total variations within a cluster. The formula to calculate the value of WCSS (for 3 clusters) is given below: • WCSS= ∑Pi in Cluster1 distance(Pi C1)2 +∑Pi in Cluster2distance(Pi C2)2+∑Pi in CLuster3 distance(Pi C3)2
- 23. • In the above formula of WCSS, • ∑Pi in Cluster1 distance(Pi C1)2: It is the sum of the square of the distances between each data point and its centroid within a cluster1 and the same for the other two terms. • To measure the distance between data points and centroid, we can use any method such as Euclidean distance or Manhattan distance.
- 24. • To find the optimal value of clusters, the elbow method follows the below steps: • It executes the K-means clustering on a given dataset for different K values (ranges from 1-10). • For each value of K, calculates the WCSS value. • Plots a curve between calculated WCSS values and the number of clusters K. • The sharp point of bend or a point of the plot looks like an arm, then that point is considered as the best value of K. • Since the graph shows the sharp bend, which looks like an elbow, hence it is known as the elbow method. The graph for the elbow method looks like the below image: •