Notes:
K-Means is a popular machine learning algorithm that is used for clustering data. It is a type of unsupervised learning algorithm, which means that it can be used to discover patterns in data without being explicitly told what the patterns are. In the case of K-Means, the algorithm groups data points into clusters based on their similarity. This can be useful for a variety of purposes, such as identifying trends or patterns in data, or for making predictions about future data.
One potential use of K-Means in the context of chatbots is to group user responses into different categories or clusters. For example, a chatbot could use K-Means to group user responses into different clusters based on their similarity. This could allow the chatbot to respond more appropriately to users by recognizing the cluster that their response belongs to, and providing a relevant response. For example, if a user’s response belongs to a cluster that contains mostly positive responses, the chatbot could respond with a positive message. This could help the chatbot to provide more personalized and effective responses to users.
See also:
- K-means clustering: how it works
- K mean clustering algorithm with solve example
- StatQuest: K-means clustering
- How to Perform K-Means Clustering in R Statistical Computing
- K-means & Image Segmentation – Computerphile
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- Introduction to K-Means Clustering
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- K Means Clustering Algorithm | K Means Example in Python | Machine Learning Algorithms | Edureka
- K-Means Clustering Algorithm – Solved Numerical Question 1(Euclidean Distance)(Hindi)
- How K-Means algorithm works
- MATLAB tutorial – k-means and hierarchical clustering
- Clustering (4): Gaussian Mixture Models and EM
- K-Means Clustering – The Math of Intelligence (Week 3)
- How to run cluster analysis in Excel
- Lecture 60 — The k Means Algorithm | Stanford University
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- Cluster Computing and MapReduce Lecture 4
- K-Means Clustering Algorithm – Cluster Analysis | Machine Learning Algorithm | Data Science |Edureka
- K Means Clustering Part – 1 | K Means Clustering Algorithm Tutorial – 1 | Data Science | Edureka
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- Unsupervised Learning: Introduction to K-mean Clustering
- (ML 16.1) K-means clustering (part 1)
- K-means Algorithm Demo
- Lecture 13.2 — Clustering | KMeans Algorithm — [ Machine Learning | Andrew Ng ]
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- Getting Started with Orange 11: k-Means
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- Fuzzy Clustering
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- How K-mean clustering groups data-: A Simple Example
- Tutorial on K Means Clustering using Weka
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- Brain Tumor Segmentation using K-means Algorithm.
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- Introduction to Fuzzy K Means | Apache Mahout | Edureka
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- K Means as Optimization Quiz – Georgia Tech – Machine Learning
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- 067 Example of KMeans with Spark
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- Limitations of K-Means Solution – Intro to Machine Learning
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- Brain Tumor Segmentation Using K-Means Clustering And Fuzzy C-Means
- K-Means 1 ?-??????? ????? ????? ?????????
- Image Segmentation with K-Means Clustering
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- Cara Mudah Mengerjakan Algoritma K-Means Di Excel