ExcelR offers 160+ Hours Classroom training to improve your skills on Business Analytics / Data Scientist / Data Analytics. The Leaders in Business Analytics
The document describes the Rough K-Means clustering algorithm. It takes a dataset as input and outputs lower and upper approximations of K clusters. It works as follows:
1. Objects are randomly assigned to initial clusters. Cluster centroids are then computed.
2. Objects are assigned to clusters based on the ratio of their distance to closest versus second closest centroid. Objects on the boundary may belong to multiple clusters.
3. Cluster centroids are recomputed based on the new cluster assignments. The process repeats until cluster centroids converge.
An example is provided to illustrate the algorithm on a sample dataset with 6 objects and 2 features.
The International Journal of Engineering and Science (The IJES)theijes
This document summarizes a research paper that proposes a novel approach to improving the k-means clustering algorithm. The standard k-means algorithm is computationally expensive and produces results that depend heavily on the initial centroid selection. The proposed approach determines initial centroids systematically and uses a heuristic to efficiently assign data points to clusters. It improves both the accuracy and efficiency of k-means clustering by ensuring the entire process takes O(n2) time without sacrificing cluster quality.
Slides for Introductory session on K Means Clustering.
simple and good. ppt
Could be used for taking classes for MCA students on Clustering Algorithms for Data mining.
Prepared By K.T.Thomas HOD of Computer Science, Santhigiri College Vazhithala
Enhance The K Means Algorithm On Spatial DatasetAlaaZ
The document describes an enhancement to the standard k-means clustering algorithm. The enhancement aims to improve computational speed by storing additional information from each iteration, such as the closest cluster and distance for each data point. This avoids needing to recompute distances to all cluster centers in subsequent iterations if a point does not change clusters. The complexity of the enhanced algorithm is reduced from O(nkl) to O(nk) where n is points, k is clusters, and l is iterations.
New Approach for K-mean and K-medoids AlgorithmEditor IJCATR
K-means and K-medoids clustering algorithms are widely used for many practical applications. Original k
medoids algorithms select initial centroids and medoids randomly that affect the quality of the resulting clusters and sometimes it
generates unstable and empty clusters which are meaningless.
expensive and requires time proportional to the product of the number of data items, number of clusters and the number of iterations.
The new approach for the k mean algorithm eliminates the deficiency of exiting k mean. It first calculates the initial centro
requirements of users and then gives better, effective and stable cluster. It also takes less execution time because it eliminates
unnecessary distance computation by using previous iteration. The new approach for k
systematically based on initial centroids. It generates stable clusters to improve accuracy.
The document discusses graph-based clustering methods. It describes how graphs can be used to represent real-world networks from domains like biology, technology, social networks, and economics. It introduces the idea of using minimal spanning trees and hierarchical clustering to identify clusters in graph data. Two common algorithms for finding minimal spanning trees are described: Prim's algorithm and Kruskal's algorithm. Different strategies for iteratively deleting branches from the minimal spanning tree are also summarized to form clusters, such as deleting the branch with the maximum weight or inconsistent branches based on a reference value.
The document describes the Rough K-Means clustering algorithm. It takes a dataset as input and outputs lower and upper approximations of K clusters. It works as follows:
1. Objects are randomly assigned to initial clusters. Cluster centroids are then computed.
2. Objects are assigned to clusters based on the ratio of their distance to closest versus second closest centroid. Objects on the boundary may belong to multiple clusters.
3. Cluster centroids are recomputed based on the new cluster assignments. The process repeats until cluster centroids converge.
An example is provided to illustrate the algorithm on a sample dataset with 6 objects and 2 features.
The International Journal of Engineering and Science (The IJES)theijes
This document summarizes a research paper that proposes a novel approach to improving the k-means clustering algorithm. The standard k-means algorithm is computationally expensive and produces results that depend heavily on the initial centroid selection. The proposed approach determines initial centroids systematically and uses a heuristic to efficiently assign data points to clusters. It improves both the accuracy and efficiency of k-means clustering by ensuring the entire process takes O(n2) time without sacrificing cluster quality.
Slides for Introductory session on K Means Clustering.
simple and good. ppt
Could be used for taking classes for MCA students on Clustering Algorithms for Data mining.
Prepared By K.T.Thomas HOD of Computer Science, Santhigiri College Vazhithala
Enhance The K Means Algorithm On Spatial DatasetAlaaZ
The document describes an enhancement to the standard k-means clustering algorithm. The enhancement aims to improve computational speed by storing additional information from each iteration, such as the closest cluster and distance for each data point. This avoids needing to recompute distances to all cluster centers in subsequent iterations if a point does not change clusters. The complexity of the enhanced algorithm is reduced from O(nkl) to O(nk) where n is points, k is clusters, and l is iterations.
New Approach for K-mean and K-medoids AlgorithmEditor IJCATR
K-means and K-medoids clustering algorithms are widely used for many practical applications. Original k
medoids algorithms select initial centroids and medoids randomly that affect the quality of the resulting clusters and sometimes it
generates unstable and empty clusters which are meaningless.
expensive and requires time proportional to the product of the number of data items, number of clusters and the number of iterations.
The new approach for the k mean algorithm eliminates the deficiency of exiting k mean. It first calculates the initial centro
requirements of users and then gives better, effective and stable cluster. It also takes less execution time because it eliminates
unnecessary distance computation by using previous iteration. The new approach for k
systematically based on initial centroids. It generates stable clusters to improve accuracy.
The document discusses graph-based clustering methods. It describes how graphs can be used to represent real-world networks from domains like biology, technology, social networks, and economics. It introduces the idea of using minimal spanning trees and hierarchical clustering to identify clusters in graph data. Two common algorithms for finding minimal spanning trees are described: Prim's algorithm and Kruskal's algorithm. Different strategies for iteratively deleting branches from the minimal spanning tree are also summarized to form clusters, such as deleting the branch with the maximum weight or inconsistent branches based on a reference value.
CC282 Unsupervised Learning (Clustering) Lecture 7 slides for ...butest
This document provides an overview of unsupervised learning techniques, specifically clustering algorithms. It discusses three main approaches to clustering: exclusive clustering using k-means, agglomerative clustering using hierarchical algorithms, and overlapping clustering using fuzzy c-means. It provides examples and explanations of how k-means and hierarchical clustering work, including the steps involved in each algorithm. It also discusses strengths and weaknesses of different clustering methods.
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
1. Clustering high-dimensional data presents unique challenges as traditional distance measures become less meaningful and clusters may only exist in subspaces of the data. 2. Subspace clustering methods aim to find clusters that exist in subspaces of the feature space rather than the entire space. 3. Popular subspace clustering methods include subspace search approaches that examine various subspaces, bi-clustering methods, and dimensionality reduction techniques.
The document discusses clustering techniques and provides details about the k-means clustering algorithm. It begins with an introduction to clustering and lists different clustering techniques. It then describes the k-means algorithm in detail, including how it works, the steps involved, and provides an example illustration. Finally, it discusses comments on the k-means algorithm, focusing on aspects like choosing the value of k, initializing cluster centroids, and different distance measurement methods.
1. The document discusses various advanced clustering analysis methods for handling high-dimensional and complex data types.
2. It covers probability-based clustering models, clustering high-dimensional data by addressing challenges like the curse of dimensionality, and clustering graphs and networks.
3. Advanced methods discussed include mixture models, model-based clustering using EM algorithm, subspace clustering to find clusters existing in subspaces, and clustering with constraints.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
The k-means clustering algorithm partitions n observations into k clusters where each observation belongs to the cluster with the nearest mean. It works by assigning every observation to a cluster whose mean yields the least within-cluster sum of squares, then recalculating the means to be the centroids of the new clusters. The algorithm iterates between these two steps until convergence is achieved. K-means clustering is commonly used for data mining and machine learning applications such as image segmentation.
This document is a seminar report on the K-Means clustering algorithm submitted by Gaurav Handa. It includes an introduction that discusses the importance of data mining and describes K-Means clustering. It also includes chapters that analyze and plan the implementation of K-Means, describe the algorithm and its flowchart, discuss limitations, and provide examples of implementing K-Means using graphs and Java code. The report was submitted in partial fulfillment of seminar requirements and includes acknowledgements and certificates.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
An improvement in k mean clustering algorithm using better time and accuracyijpla
This document summarizes a research paper that proposes an improved K-means clustering algorithm to enhance accuracy and reduce computation time. The standard K-means algorithm randomly selects initial cluster centroids, affecting results. The proposed algorithm systematically determines initial centroids based on data point distances. It assigns data to the closest initial centroid to generate initial clusters. Iteratively, it calculates new centroids and reassigns data only if distances decrease, reducing unnecessary computations. Experiments on various datasets show the proposed algorithm achieves higher accuracy faster than standard K-means.
Pattern recognition binoy k means clustering108kaushik
This document discusses clustering and the k-means clustering algorithm. It defines clustering as grouping a set of data objects into clusters so that objects within the same cluster are similar to each other but dissimilar to objects in other clusters. The k-means algorithm is described as an iterative process that assigns each object to one of k predefined clusters based on the object's distance from the cluster's centroid, then recalculates the centroid, repeating until cluster assignments no longer change. A worked example demonstrates how k-means partitions 7 objects into 2 clusters over 3 iterations. The k-means algorithm is noted to be efficient but requires specifying k and can be impacted by outliers, noise, and non-convex cluster shapes.
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.
Cluster analysis is an unsupervised machine learning technique used to group unlabeled data points into clusters based on similarities. It involves finding groups of objects such that objects within a cluster are more similar to each other than objects in different clusters. The key goals of cluster analysis are to maximize intra-cluster similarity while minimizing inter-cluster similarity. Common applications of cluster analysis include market segmentation, document classification, and identifying homogeneous groups in biological data.
K-Means clustering uses an iterative procedure which is very much sensitive and dependent upon the initial centroids. The initial centroids in the k-means clustering are chosen randomly, and hence the clustering also changes with respect to the initial centroids. This paper tries to overcome this problem of random selection of centroids and hence change of clusters with a premeditated selection of initial centroids. We have used the iris, abalone and wine data sets to demonstrate that the proposed method of finding the initial centroids and using the centroids in k-means algorithm improves the clustering performance. The clustering also remains the same in every run as the initial centroids are not randomly selected but through premeditated method.
Data science involves using scientific methods to extract knowledge from structured and unstructured data. Machine learning is a type of data science that uses examples to help computers learn without being explicitly programmed. It detects patterns in data and adjusts programs accordingly. Machine learning algorithms include supervised learning techniques like decision trees and random forests as well as unsupervised learning techniques like clustering. Hierarchical and k-means clustering are commonly used clustering algorithms. Hierarchical clustering groups objects into clusters based on their distances while k-means clustering assigns objects to k number of clusters based on their attributes.
The document discusses various clustering approaches including partitioning, hierarchical, density-based, grid-based, model-based, frequent pattern-based, and constraint-based methods. It focuses on partitioning methods such as k-means and k-medoids clustering. K-means clustering aims to partition objects into k clusters by minimizing total intra-cluster variance, representing each cluster by its centroid. K-medoids clustering is a more robust variant that represents each cluster by its medoid or most centrally located object. The document also covers algorithms for implementing k-means and k-medoids clustering.
The document discusses implementing an integrated approach of the K-means clustering algorithm for prediction analysis. It begins with motivating the need to improve the accuracy and dependability of existing overlapping K-means clustering by removing its dependency on random initialization parameters. The proposed methodology determines the optimal number of clusters K based on the dataset, calculates initial centroid positions using a harmonic means method, and applies overlapping K-means clustering. The implementation and results on two large datasets show the integrated approach outperforms original overlapping K-means in terms of accuracy, F-measure, Rand index, and number of iterations.
Chapter 10. Cluster Analysis Basic Concepts and Methods.pptSubrata Kumer Paul
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
This document describes the K-means clustering algorithm. It begins by defining cluster analysis and its goal of grouping similar objects together. It then explains that K-means is a partitioning clustering method that assigns data points to K clusters based on minimizing distances between points and assigned cluster centroids. The document provides details on initializing centroids, assigning points, updating centroids, and determining convergence. It also discusses evaluating clusters and limitations of K-means. Finally, it provides examples of applying K-means to image segmentation and anomaly detection in wind turbine data.
Our Business Analytics certification training course is designed by the industry experts, which is precisely tailored for the professionals who wants to pursue a career as a Data Scientist in job market.
ExcelR offers 160 hours classroom training on Business Analytics / Data Scientist / Data Analytics. We are considered as one of the best training institutes on Business Analytics in Hyderabad. “Faculty and vast course agenda is our differentiator”.
CC282 Unsupervised Learning (Clustering) Lecture 7 slides for ...butest
This document provides an overview of unsupervised learning techniques, specifically clustering algorithms. It discusses three main approaches to clustering: exclusive clustering using k-means, agglomerative clustering using hierarchical algorithms, and overlapping clustering using fuzzy c-means. It provides examples and explanations of how k-means and hierarchical clustering work, including the steps involved in each algorithm. It also discusses strengths and weaknesses of different clustering methods.
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
1. Clustering high-dimensional data presents unique challenges as traditional distance measures become less meaningful and clusters may only exist in subspaces of the data. 2. Subspace clustering methods aim to find clusters that exist in subspaces of the feature space rather than the entire space. 3. Popular subspace clustering methods include subspace search approaches that examine various subspaces, bi-clustering methods, and dimensionality reduction techniques.
The document discusses clustering techniques and provides details about the k-means clustering algorithm. It begins with an introduction to clustering and lists different clustering techniques. It then describes the k-means algorithm in detail, including how it works, the steps involved, and provides an example illustration. Finally, it discusses comments on the k-means algorithm, focusing on aspects like choosing the value of k, initializing cluster centroids, and different distance measurement methods.
1. The document discusses various advanced clustering analysis methods for handling high-dimensional and complex data types.
2. It covers probability-based clustering models, clustering high-dimensional data by addressing challenges like the curse of dimensionality, and clustering graphs and networks.
3. Advanced methods discussed include mixture models, model-based clustering using EM algorithm, subspace clustering to find clusters existing in subspaces, and clustering with constraints.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
The k-means clustering algorithm partitions n observations into k clusters where each observation belongs to the cluster with the nearest mean. It works by assigning every observation to a cluster whose mean yields the least within-cluster sum of squares, then recalculating the means to be the centroids of the new clusters. The algorithm iterates between these two steps until convergence is achieved. K-means clustering is commonly used for data mining and machine learning applications such as image segmentation.
This document is a seminar report on the K-Means clustering algorithm submitted by Gaurav Handa. It includes an introduction that discusses the importance of data mining and describes K-Means clustering. It also includes chapters that analyze and plan the implementation of K-Means, describe the algorithm and its flowchart, discuss limitations, and provide examples of implementing K-Means using graphs and Java code. The report was submitted in partial fulfillment of seminar requirements and includes acknowledgements and certificates.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
An improvement in k mean clustering algorithm using better time and accuracyijpla
This document summarizes a research paper that proposes an improved K-means clustering algorithm to enhance accuracy and reduce computation time. The standard K-means algorithm randomly selects initial cluster centroids, affecting results. The proposed algorithm systematically determines initial centroids based on data point distances. It assigns data to the closest initial centroid to generate initial clusters. Iteratively, it calculates new centroids and reassigns data only if distances decrease, reducing unnecessary computations. Experiments on various datasets show the proposed algorithm achieves higher accuracy faster than standard K-means.
Pattern recognition binoy k means clustering108kaushik
This document discusses clustering and the k-means clustering algorithm. It defines clustering as grouping a set of data objects into clusters so that objects within the same cluster are similar to each other but dissimilar to objects in other clusters. The k-means algorithm is described as an iterative process that assigns each object to one of k predefined clusters based on the object's distance from the cluster's centroid, then recalculates the centroid, repeating until cluster assignments no longer change. A worked example demonstrates how k-means partitions 7 objects into 2 clusters over 3 iterations. The k-means algorithm is noted to be efficient but requires specifying k and can be impacted by outliers, noise, and non-convex cluster shapes.
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.
Cluster analysis is an unsupervised machine learning technique used to group unlabeled data points into clusters based on similarities. It involves finding groups of objects such that objects within a cluster are more similar to each other than objects in different clusters. The key goals of cluster analysis are to maximize intra-cluster similarity while minimizing inter-cluster similarity. Common applications of cluster analysis include market segmentation, document classification, and identifying homogeneous groups in biological data.
K-Means clustering uses an iterative procedure which is very much sensitive and dependent upon the initial centroids. The initial centroids in the k-means clustering are chosen randomly, and hence the clustering also changes with respect to the initial centroids. This paper tries to overcome this problem of random selection of centroids and hence change of clusters with a premeditated selection of initial centroids. We have used the iris, abalone and wine data sets to demonstrate that the proposed method of finding the initial centroids and using the centroids in k-means algorithm improves the clustering performance. The clustering also remains the same in every run as the initial centroids are not randomly selected but through premeditated method.
Data science involves using scientific methods to extract knowledge from structured and unstructured data. Machine learning is a type of data science that uses examples to help computers learn without being explicitly programmed. It detects patterns in data and adjusts programs accordingly. Machine learning algorithms include supervised learning techniques like decision trees and random forests as well as unsupervised learning techniques like clustering. Hierarchical and k-means clustering are commonly used clustering algorithms. Hierarchical clustering groups objects into clusters based on their distances while k-means clustering assigns objects to k number of clusters based on their attributes.
The document discusses various clustering approaches including partitioning, hierarchical, density-based, grid-based, model-based, frequent pattern-based, and constraint-based methods. It focuses on partitioning methods such as k-means and k-medoids clustering. K-means clustering aims to partition objects into k clusters by minimizing total intra-cluster variance, representing each cluster by its centroid. K-medoids clustering is a more robust variant that represents each cluster by its medoid or most centrally located object. The document also covers algorithms for implementing k-means and k-medoids clustering.
The document discusses implementing an integrated approach of the K-means clustering algorithm for prediction analysis. It begins with motivating the need to improve the accuracy and dependability of existing overlapping K-means clustering by removing its dependency on random initialization parameters. The proposed methodology determines the optimal number of clusters K based on the dataset, calculates initial centroid positions using a harmonic means method, and applies overlapping K-means clustering. The implementation and results on two large datasets show the integrated approach outperforms original overlapping K-means in terms of accuracy, F-measure, Rand index, and number of iterations.
Chapter 10. Cluster Analysis Basic Concepts and Methods.pptSubrata Kumer Paul
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Similar to business analytics course in delhi (19)
This document describes the K-means clustering algorithm. It begins by defining cluster analysis and its goal of grouping similar objects together. It then explains that K-means is a partitioning clustering method that assigns data points to K clusters based on minimizing distances between points and assigned cluster centroids. The document provides details on initializing centroids, assigning points, updating centroids, and determining convergence. It also discusses evaluating clusters and limitations of K-means. Finally, it provides examples of applying K-means to image segmentation and anomaly detection in wind turbine data.
Our Business Analytics certification training course is designed by the industry experts, which is precisely tailored for the professionals who wants to pursue a career as a Data Scientist in job market.
ExcelR offers 160 hours classroom training on Business Analytics / Data Scientist / Data Analytics. We are considered as one of the best training institutes on Business Analytics in Hyderabad. “Faculty and vast course agenda is our differentiator”.
ExcelR offers 160 hours classroom training on Business Analytics / Data Scientist / Data Analytics. We are considered as one of the best training institutes on Business Analytics in Hyderabad. “Faculty and vast course agenda is our differentiator”.
ExcelR offers 160 hours classroom training on Business Analytics / Data Scientist / Data Analytics. We are considered as one of the best training institutes on Business Analytics in Hyderabad. “Faculty and vast course agenda is our differentiator”.
This document provides information about the requirements and exam pattern for the PMI-ACP Agile Certified Practitioner certification. The exam contains 120 multiple-choice questions that must be completed within 3 hours, with 50% of the questions covering Agile tools and techniques and 50% covering Agile knowledge and skills. Candidates must have a combination of education and professional experience in general project management, Agile project management, and the required number of contact hours and experience hours varies depending on the candidate's level of education.
ExcelR has trainers who have over 15 years of experience on an average, in Agile methodology process implementation, managing, playing role of Agile coach etc. This will ensure that you get the best from the best.
ExcelR offers 160+ Hours Classroom training to improve your skills on Business Analytics / Data Scientist / Data Analytics. The Leaders in Business Analytics
ExcelR offers 160+ Hours Classroom training to improve your skills on Business Analytics / Data Scientist / Data Analytics. The Leaders in Business Analytics
ExcelR offers 160+ Hours Classroom training to improve your skills on Business Analytics / Data Scientist / Data Analytics. The Leaders in Business Analytics
Poisson regression is used when the output variable is a count that follows a Poisson distribution and the variance equals the mean. It can be used to model phenomena like the number of occurrences per time period, people in lines, or awards earned. Examples include modeling the number of military deaths by kicks per year, people in grocery store lines based on sales and events, and number of awards earned by students based on their program and math scores. The data described is a sample where the number of awards earned by students is the outcome variable, their math scores and program type are predictor variables.
Our Business Analytics certification training course is designed by the industry experts, which is precisely tailored for the professionals who wants to pursue a career as a Data Scientist in job market.
Our Business Analytics certification training course is designed by the industry experts, which is precisely tailored for the professionals who wants to pursue a career as a Data Scientist in job market.
ExcelR offers 160+ Hours Classroom training to improve your skills on Business Analytics / Data Scientist / Data Analytics. The Leaders in Business Analytics
ExcelR offers 160+ Hours Classroom training to improve your skills on Business Analytics / Data Scientist / Data Analytics. The Leaders in Business Analytics
ExcelR offers training on PMI Agile Certification which clearly explains the Agile methodologies and techniques for managing successful project completion
ExcelR offers training on PMI Agile Certification which clearly explains the Agile methodologies and techniques for managing successful project completion
ExcelR offers 160+ Hours Classroom training to improve your skills on Business Analytics / Data Scientist / Data Analytics. The Leaders in Business Analytics
The document provides an overview of time series forecasting. It discusses:
- Why forecasting is important and examples where it is used
- Components of time series data like trends, seasonality, and noise
- Graphical representations used in time series analysis like time plots, scatter plots, lag plots, and autocorrelation function (ACF) plots
- Key steps in a forecasting strategy including defining goals, data collection, exploring the series, and selecting forecasting methods
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Training: ISO/IEC 27001 Information Security Management System - EN | PECB
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LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
business analytics course in delhi
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K-means Algorithmg
Cluster Analysis in Data Mining
Presented by Zijun Zhang
Algorithm Description
What is Cluster Analysis?
Cluster analysis groups data objects based only on
information found in data that describes the objects and their
relationships.
Goal of Cluster Analysis
The objects within a group be similar to one another andj g p
different from the objects in other groups
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Algorithm Description
Types of Clustering
Partitioning and Hierarchical Clustering
Hierarchical Clustering
- A set of nested clusters organized as a hierarchical tree
Partitioning Clusteringg g
- A division data objects into non-overlapping subsets
(clusters) such that each data object is in exactly one subset
Algorithm Description
p4
p1
p3
p2
A Partitional Clustering Hierarchical Clustering
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Algorithm Description
What is K-means?
1. Partitional clustering approach
2. Each cluster is associated with a centroid (center point)
3. Each point is assigned to the cluster with the closest centroid
4 Number of clusters K must be specified4. Number of clusters, K, must be specified
Algorithm Statement
Basic Algorithm of K-means
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Algorithm Statement
Details of K-means
1 Initial centroids are often chosen randomly1. Initial centroids are often chosen randomly.
- Clusters produced vary from one run to another
2. The centroid is (typically) the mean of the points in the cluster.
3.‘Closeness’ is measured by Euclidean distance, cosine similarity, correlation,
etc.
4. K-means will converge for common similarity measures mentioned above.
5. Most of the convergence happens in the first few iterations.5. Most of the convergence happens in the first few iterations.
- Often the stopping condition is changed to ‘Until relatively few points
change clusters’
Algorithm Statement
Euclidean Distance
A simple example: Find the distance between two points, the original
and the point (3,4)
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Algorithm Statement
Update Centroid
We use the following equation to calculate the n dimensionalWe use the following equation to calculate the n dimensional
centroid point amid k n-dimensional points
Example: Find the centroid of 3 2D points, (2,4), (5,2)
and (8,9)and (8,9)
Example of K-means
Select three initial centroids
1
1.5
2
2.5
3
y
Iteration 1
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
0
0.5
x
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Example of K-means
Assigning the points to nearest K clusters and re-compute the
centroids
1
1.5
2
2.5
3
y
Iteration 3
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
0
0.5
x
Example of K-means
K-means terminates since the centroids converge to certain points
and do not change.
1
1.5
2
2.5
3
y
Iteration 6
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
0
0.5
x
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Example of K-means
2
2.5
3
Iteration 1
2
2.5
3
Iteration 2
2
2.5
3
Iteration 3
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
0
0.5
1
1.5
x
y
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
0
0.5
1
1.5
x
y
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
0
0.5
1
1.5
x
y
3
Iteration 4
3
Iteration 5
3
Iteration 6
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
0
0.5
1
1.5
2
2.5
x
y
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
0
0.5
1
1.5
2
2.5
x
y
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
0
0.5
1
1.5
2
2.5
x
y
Example of K-means
Demo of K-means
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Evaluating K-means Clusters
Most common measure is Sum of Squared Error (SSE)
For each point, the error is the distance to the nearest cluster
To get SSE we square these errors and sum them To get SSE, we square these errors and sum them.
x is a data point in cluster Ci and mi is the representative point for cluster
Ci
can show that mi corresponds to the center (mean) of the cluster
Given two clusters we can choose the one with the smallest error
K
i Cx
i
i
xmdistSSE
1
2
),(
Given two clusters, we can choose the one with the smallest error
One easy way to reduce SSE is to increase K, the number of clusters
A good clustering with smaller K can have a lower SSE than a poor
clustering with higher K
Problem about K
How to choose K?
1. Use another clustering method, like EM.
2. Run algorithm on data with several different values of K.
3. Use the prior knowledge about the characteristics of the problem.
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Problem about initialize centers
How to initialize centers?
- Random Points in Feature Space
- Random Points From Data Set
- Look For Dense Regions of Space
- Space them uniformly around the feature space
Cluster Quality
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Cluster Quality
Limitation of K-means
K-means has problems when clusters are of
differingg
Sizes
Densities
Non-globular shapes
K h bl h h d i K-means has problems when the data contains
outliers.
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Limitation of K-means
Original Points K-means (3 Clusters)
Application of K-means
Image Segmentation
The k-means clustering algorithm is commonly used in
computer vision as a form of image segmentation. The
results of the segmentation are used to aid border detection
and object recognition.
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K-means in Wind Energy
Clustering can be applied to detect
b lit i i d d t ( b labnormality in wind data (abnormal
vibration)
Monitor Wind Turbine Conditions
Beneficial to preventative maintenance
K means can be more powerful and K-means can be more powerful and
applicable after appropriate modifications
K-means in Wind Energy
Modified K-means
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K-means in Wind Energy
Clustering cost function
2
1
1
( , , )
j i
k
j i
i C
d k
n
x
x c x c
1
k
i
i
n m
21 k
1
1
1
( , , )
j i
j ik
i C
i
i
d k
m
x
x c x c
K-means in Wind Energy
Determination of k value
0 02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
Costofclustering
0
0.01
0.02
2 3 4 5 6 7 8 9 10 11 12 13
Numberof clusters
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K-means in Wind Energy
Summary of clustering result
No. of Cluster c1 (Drive train acc.) c2 (Wind speed) Number of points Percentage (%)
1 71.9612 9.97514 313 8.75524
2 65.8387 9.42031 295 8.25175
3 233.9184 9.57990 96 2.68531
4 17.4187 7.13375 240 6.71329
5 3.3706 8.99211 437 12.22378
6 0.3741 0.40378 217 6.06993
7 18.1361 8.09900 410 11.46853
8 0.7684 10.56663 419 11.72028
9 62.0493 8.81445 283 7.91608
10 81.7522 10.67867 181 5.06294
11 83.8067 8.10663 101 2.82517
12 0.9283 9.78571 583 16.30769
K-means in Wind Energy
Visualization of monitoring result
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K-means in Wind Energy
Visualization of vibration under normal condition
14
4
6
8
10
12
14
Windspeed(m/s)
0
2
0 20 40 60 80 100 120 140
Drive train acceleration
Reference
1. Introduction to Data Mining, P.N. Tan, M. Steinbach, V. Kumar, Addison Wesley
2. An efficient k-means clustering algorithm: Analysis and implementation, T. Kanungo, D. M.
Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Y. Wu, IEEE Trans. PatternAnalysis
and Machine Intelligence, 24 (2002), 881-892
3. http://www.cs.cmu.edu/~cga/ai-course/kmeans.pdf
4. http://www.cse.msstate.edu/~url/teaching/CSE6633Fall08/lec16%20k-means.pdf
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Appendix One
Original Points K-means (2 Clusters)
Appendix Two
Original Points K-means Clusters
One solution is to use many clusters.
Find parts of clusters, but need to put together.