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This document discusses and provides code examples for the K-means clustering algorithm. It includes pseudocode that describes the basic K-means algorithm with complexity of O(k*n/p). It also provides code examples for implementing K-means in SQL, R, Java MapReduce, and Java reducers.
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Neural nw k means
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
K-means Clustering Algorithm with Matlab Source code
The K-means clustering algorithm partitions observations into K clusters by minimizing the distance between observations and cluster centroids. It works by randomly assigning observations to K clusters, calculating the distance between each observation and centroid, reassigning observations to their closest centroid, and repeating until cluster assignments are stable. Common distance measures used include Euclidean, squared Euclidean, and Manhattan distances. The algorithm aims to group similar observations together based on feature similarity to reduce the size of codebooks for applications like speech processing.
K-Means manual work
The k-means clustering algorithm takes as input the number of clusters k and a set of data points, and assigns each data point to one of k clusters. It works by first randomly selecting k data points as initial cluster centroids. It then assigns each remaining point to the closest centroid, and recalculates the centroid positions. This process repeats until the centroids are stable or a stopping criteria is reached. As an example, the document applies k-means to cluster 6 data points into 2 groups, showing the random selection of initial centroids, assignment of points, and recalculation of centroids over multiple steps.
K mean-clustering algorithm
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
K means clustring @jax
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This document discusses the k-means clustering method. It begins by defining the problem of cluster analysis as dividing data points into groups to minimize the sum of distances between points and their assigned cluster centers. It then describes the main k-means algorithms and outlines the iterative process of assigning points to the nearest cluster center and recalculating the centers. Finally, it provides an example applying k-means clustering to sample data and analyzing the results.
K mean-clustering
K-means clustering is an algorithm that partitions n observations into k clusters by minimizing the within-cluster sum of squares. It assigns each observation to the cluster with the nearest mean. The algorithm works by iteratively updating cluster means and reassigning observations until convergence is reached. Some key applications of k-means clustering include machine learning, data mining, image segmentation, and choosing color palettes. However, it has weaknesses such as dependency on initial conditions and requirement to pre-specify the number of clusters k.
Intro to MATLAB and K-mean algorithm
Introductory session for basic matlab commands and a brief overview of K-mean clustering algorithm with image processing example.
NOTE: you can find code of k-mean clustering algorithm for image processing in notes.
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Neural nw k means
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
K-means Clustering Algorithm with Matlab Source code
The K-means clustering algorithm partitions observations into K clusters by minimizing the distance between observations and cluster centroids. It works by randomly assigning observations to K clusters, calculating the distance between each observation and centroid, reassigning observations to their closest centroid, and repeating until cluster assignments are stable. Common distance measures used include Euclidean, squared Euclidean, and Manhattan distances. The algorithm aims to group similar observations together based on feature similarity to reduce the size of codebooks for applications like speech processing.
K-Means manual work
The k-means clustering algorithm takes as input the number of clusters k and a set of data points, and assigns each data point to one of k clusters. It works by first randomly selecting k data points as initial cluster centroids. It then assigns each remaining point to the closest centroid, and recalculates the centroid positions. This process repeats until the centroids are stable or a stopping criteria is reached. As an example, the document applies k-means to cluster 6 data points into 2 groups, showing the random selection of initial centroids, assignment of points, and recalculation of centroids over multiple steps.
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k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
K means clustring @jax
1-What is Clustering
2-Types of Clustering
3-implementation
4-Algorithm
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6-Advantage
7-Disadvantage
9-Application
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This document discusses the k-means clustering method. It begins by defining the problem of cluster analysis as dividing data points into groups to minimize the sum of distances between points and their assigned cluster centers. It then describes the main k-means algorithms and outlines the iterative process of assigning points to the nearest cluster center and recalculating the centers. Finally, it provides an example applying k-means clustering to sample data and analyzing the results.
K mean-clustering
K-means clustering is an algorithm that partitions n observations into k clusters by minimizing the within-cluster sum of squares. It assigns each observation to the cluster with the nearest mean. The algorithm works by iteratively updating cluster means and reassigning observations until convergence is reached. Some key applications of k-means clustering include machine learning, data mining, image segmentation, and choosing color palettes. However, it has weaknesses such as dependency on initial conditions and requirement to pre-specify the number of clusters k.
Intro to MATLAB and K-mean algorithm
Introductory session for basic matlab commands and a brief overview of K-mean clustering algorithm with image processing example.
NOTE: you can find code of k-mean clustering algorithm for image processing in notes.
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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.
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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.
K mean-clustering
This document provides an introduction to k-means clustering, including:
1. K-means clustering aims to partition n observations into k clusters by minimizing the within-cluster sum of squares, where each observation belongs to the cluster with the nearest mean.
2. The k-means algorithm initializes cluster centroids and assigns observations to the nearest centroid, recomputing centroids until convergence.
3. K-means clustering is commonly used for applications like machine learning, data mining, and image segmentation due to its efficiency, though it is sensitive to initialization and assumes spherical clusters.
K means clustering
This document summarizes the K-means clustering algorithm. It provides an outline of the topics covered, which include an introduction to clustering and K-means, how to calculate K-means using steps 0 through 2, results and suggestions, and references. It then provides more detail on the three steps of K-means: 1) initialize centroids, 2) assign points to closest centroids, and 3) recalculate centroids. Pseudocode is provided to demonstrate how to code K-means in Visual Basic.
K MEANS CLUSTERING
This document outlines topics to be covered in a presentation on K-means clustering. It will discuss the introduction of K-means clustering, how the algorithm works, provide an example, and applications. The key aspects are that K-means clustering partitions data into K clusters based on similarity, assigns data points to the closest centroid, and recalculates centroids until clusters are stable. It is commonly used for market segmentation, computer vision, astronomy, and agriculture.
K-means clustering algorithm
K-means clustering is an algorithm that groups data points into k clusters based on their similarity, with each point assigned to the cluster with the nearest mean. It works by randomly selecting k cluster centroids and then iteratively assigning data points to the closest centroid and recalculating the centroids until convergence. K-means clustering is fast, efficient, and commonly used for vector quantization, image segmentation, and discovering customer groups in marketing. Its runtime complexity is O(t*k*n) where t is the number of iterations, k is the number of clusters, and n is the number of data points.
K means
K-means is an unsupervised learning algorithm that clusters data by minimizing distances between data points and cluster centers. It works by:
1. Randomly selecting K data points as initial cluster centers
2. Calculating the distance between each data point and cluster center and assigning the point to the closest center
3. Re-calculating the cluster centers based on the current assignments
4. Repeating steps 2-3 until cluster centers stop moving or a maximum number of iterations is reached.
The number of clusters K must be specified beforehand but the elbow method can help determine an appropriate value for K. Bisecting K-means is an alternative that starts with all data in one cluster and recursively splits clusters
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The document discusses the K-means clustering algorithm, an unsupervised learning technique that groups unlabeled data points into K clusters based on their similarities. It works by randomly initializing K cluster centroids and then iteratively assigning data points to their nearest centroid and recalculating the centroid positions based on the new assignments until convergence is reached. The document notes that K-means clustering requires specifying the number of clusters K in advance and presents the elbow method as a way to help determine an appropriate value of K for a given dataset.
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K means clustering
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
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 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.
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K means clustering | K Means ++
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This document provides an overview of clustering techniques. It defines clustering as grouping a set of similar objects into classes, with objects within a cluster being similar to each other and dissimilar to objects in other clusters. The document then discusses partitioning, hierarchical, and density-based clustering methods. It also covers mathematical elements of clustering like partitions, distances, and data types. The goal of clustering is to minimize a similarity function to create high similarity within clusters and low similarity between clusters.
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The document summarizes hierarchical clustering techniques. It discusses two main types of hierarchical clustering - agglomerative and divisive. It presents an example dendrogram to illustrate hierarchical clustering. It also summarizes a research paper on a new algorithm called CLUBS that performs faster and more accurate hierarchical clustering compared to existing algorithms. The document concludes by discussing experiments applying hierarchical clustering on two biomedical datasets containing gene expression data to group patients and cell samples.
Project PPT
The document describes a k-means clustering algorithm for outlier detection in data mining. It introduces k-means clustering and its steps. A leader-follower technique is used to determine the optimal number of clusters k. The algorithm is implemented on a sample dataset to cluster data points and identify outlier clusters based on having significantly fewer points than other clusters. The results show the data points clustered into three groups, with one cluster identified as an outlier based on its smaller size.
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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.
Enhance The K Means Algorithm On Spatial Dataset
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.
K mean-clustering
This document provides an introduction to k-means clustering, including:
1. K-means clustering aims to partition n observations into k clusters by minimizing the within-cluster sum of squares, where each observation belongs to the cluster with the nearest mean.
2. The k-means algorithm initializes cluster centroids and assigns observations to the nearest centroid, recomputing centroids until convergence.
3. K-means clustering is commonly used for applications like machine learning, data mining, and image segmentation due to its efficiency, though it is sensitive to initialization and assumes spherical clusters.
K means clustering
This document summarizes the K-means clustering algorithm. It provides an outline of the topics covered, which include an introduction to clustering and K-means, how to calculate K-means using steps 0 through 2, results and suggestions, and references. It then provides more detail on the three steps of K-means: 1) initialize centroids, 2) assign points to closest centroids, and 3) recalculate centroids. Pseudocode is provided to demonstrate how to code K-means in Visual Basic.
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This document outlines topics to be covered in a presentation on K-means clustering. It will discuss the introduction of K-means clustering, how the algorithm works, provide an example, and applications. The key aspects are that K-means clustering partitions data into K clusters based on similarity, assigns data points to the closest centroid, and recalculates centroids until clusters are stable. It is commonly used for market segmentation, computer vision, astronomy, and agriculture.
K-means clustering algorithm
K-means clustering is an algorithm that groups data points into k clusters based on their similarity, with each point assigned to the cluster with the nearest mean. It works by randomly selecting k cluster centroids and then iteratively assigning data points to the closest centroid and recalculating the centroids until convergence. K-means clustering is fast, efficient, and commonly used for vector quantization, image segmentation, and discovering customer groups in marketing. Its runtime complexity is O(t*k*n) where t is the number of iterations, k is the number of clusters, and n is the number of data points.
K means
K-means is an unsupervised learning algorithm that clusters data by minimizing distances between data points and cluster centers. It works by:
1. Randomly selecting K data points as initial cluster centers
2. Calculating the distance between each data point and cluster center and assigning the point to the closest center
3. Re-calculating the cluster centers based on the current assignments
4. Repeating steps 2-3 until cluster centers stop moving or a maximum number of iterations is reached.
The number of clusters K must be specified beforehand but the elbow method can help determine an appropriate value for K. Bisecting K-means is an alternative that starts with all data in one cluster and recursively splits clusters
Data miningpresentation
The document compares and summarizes several clustering algorithms, including K-Means, DBSCAN, hierarchical clustering, and CURE. It discusses the time and space complexity of each algorithm, how they are affected by the use of KD trees, their benefits and limitations, and provides examples of their performance on benchmark datasets.
K-means Clustering
The document discusses the K-means clustering algorithm, an unsupervised learning technique that groups unlabeled data points into K clusters based on their similarities. It works by randomly initializing K cluster centroids and then iteratively assigning data points to their nearest centroid and recalculating the centroid positions based on the new assignments until convergence is reached. The document notes that K-means clustering requires specifying the number of clusters K in advance and presents the elbow method as a way to help determine an appropriate value of K for a given dataset.
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K means clustering
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
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.
CC282 Unsupervised Learning (Clustering) Lecture 7 slides for ...
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.
Clustering part 1
Basic clustering algorithms and methods - describing hierarchical clustering method and k-means clustering method.
K means clustering | K Means ++
k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining.
Clustering: A Survey
This document provides an overview of clustering techniques. It defines clustering as grouping a set of similar objects into classes, with objects within a cluster being similar to each other and dissimilar to objects in other clusters. The document then discusses partitioning, hierarchical, and density-based clustering methods. It also covers mathematical elements of clustering like partitions, distances, and data types. The goal of clustering is to minimize a similarity function to create high similarity within clusters and low similarity between clusters.
K means clustering algorithm
Enhancement of K-means Algorithm by reducing the number of iterations, time complexity and Error Sum of Squares (SSE).
K means Clustering Algorithm
K-means clustering is an algorithm that groups data points into k number of clusters based on their similarity. It works by randomly selecting k data points as initial cluster centroids and then assigning each remaining point to the closest centroid. It then recalculates the centroids and reassigns points in an iterative process until centroids stabilize. While efficient, k-means clustering has weaknesses in that it requires specifying k, can get stuck in local optima, and is not suitable for non-convex shaped clusters or noisy data.
Hierarchical clustering techniques
The document summarizes hierarchical clustering techniques. It discusses two main types of hierarchical clustering - agglomerative and divisive. It presents an example dendrogram to illustrate hierarchical clustering. It also summarizes a research paper on a new algorithm called CLUBS that performs faster and more accurate hierarchical clustering compared to existing algorithms. The document concludes by discussing experiments applying hierarchical clustering on two biomedical datasets containing gene expression data to group patients and cell samples.
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Project PPT
The document describes a k-means clustering algorithm for outlier detection in data mining. It introduces k-means clustering and its steps. A leader-follower technique is used to determine the optimal number of clusters k. The algorithm is implemented on a sample dataset to cluster data points and identify outlier clusters based on having significantly fewer points than other clusters. The results show the data points clustered into three groups, with one cluster identified as an outlier based on its smaller size.
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This document summarizes the DBSCAN clustering algorithm. DBSCAN finds clusters based on density, requiring only two parameters: Eps, which defines the neighborhood distance, and MinPts, the minimum number of points required to form a cluster. It can discover clusters of arbitrary shape. The algorithm works by expanding clusters from core points, which have at least MinPts points within their Eps-neighborhood. Points that are not part of any cluster are classified as noise. Applications include spatial data analysis, image segmentation, and automatic border detection in medical images.
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- Common hierarchical clustering algorithms include single linkage (minimum distance), complete linkage (maximum distance), group average, and Ward's method. They differ in how they calculate distance between clusters during merging.
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K-Means Algorithm
Part of the course "Algorithmic Methods of Data Science". Sapienza University of Rome, 2015.
http://aris.me/index.php/data-mining-ds-2015
K-Means, its Variants and its Applications
This presentation was given by our project group at the Lead College competition at Shivaji University. Our project got the 1st Prize. We focused mainly on Rough K-Means and build a Social-Network-Recommender System based on Rough K-Means.
The Members of the Project group were -
Mansi Kulkarni,
Nikhil Ingole,
Prasad Mohite,
Varad Meru
Vishal Bhavsar.
Wonderful Experience !!!
K means Clustering
The document discusses clustering and k-means clustering algorithms. It provides examples of scenarios where clustering can be used, such as placing cell phone towers or opening new offices. It then defines clustering as organizing data into groups where objects within each group are similar to each other and dissimilar to objects in other groups. The document proceeds to explain k-means clustering, including the process of initializing cluster centers, assigning data points to the closest center, recomputing the centers, and iterating until centers converge. It provides a use case of using k-means to determine locations for new schools.
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The document discusses various clustering algorithms and concepts:
1) K-means clustering groups data by minimizing distances between points and cluster centers, but it is sensitive to initialization and may find local optima.
2) K-medians clustering is similar but uses point medians instead of means as cluster representatives.
3) K-center clustering aims to minimize maximum distances between points and clusters, and can be approximated with a farthest-first traversal algorithm.
Chapter 11. Cluster Analysis Advanced Methods.ppt
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
Data Mining: Concepts and techniques: Chapter 11,Review: Basic Cluster Analys...
slides contain:
Cluster Analysis: Basic Concepts,
Partitioning Methods,
Hierarchical Methods,
Density-Based Methods,
Grid-Based Methods,
Evaluation of Clustering,
by
Jiawei Han, Micheline Kamber, and Jian Pei,
University of Illinois at Urbana-Champaign &
Simon Fraser University,
©2013 Han, Kamber & Pei. All rights reserved.
CMUデータベース輪読会第8回
CMUデータベース輪読会 第8回 Storage Models, Data Layout, & System Catalogs 紹介資料
https://database-paper-reading.connpass.com/event/225073/
11 clusadvanced
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.
11ClusAdvanced.ppt
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.
k-mean-clustering.ppt
K-means clustering is an algorithm that partitions n observations into k clusters, where each observation belongs to the cluster with the nearest mean. It works by assigning each observation to a cluster whose mean yields the least within-cluster sum of squares, then recalculating the mean of each cluster. This process repeats until cluster means converge. K-means clustering is commonly used in data mining applications and has been applied to areas like image segmentation, vector quantization, and astronomy.
k-mean-Clustering impact on AI using DSS
K-means clustering is an algorithm that partitions n observations into k clusters by minimizing the within-cluster sum of squares. It assigns each observation to the cluster with the nearest mean. The algorithm works by iteratively updating cluster means and reassigning observations until convergence is reached. Some key applications of k-means clustering include machine learning, data mining, image segmentation, and choosing color palettes. However, it has weaknesses such as dependency on initial cluster means and sensitivity to outliers.
Similar to K means (20)
The International Journal of Engineering and Science (The IJES)
The International Journal of Engineering and Science (The IJES)
An improvement in k mean clustering algorithm using better time and accuracy
An improvement in k mean clustering algorithm using better time and accuracy
Parallel Implementation of K Means Clustering on CUDA
Parallel Implementation of K Means Clustering on CUDA
Optimising Data Using K-Means Clustering Algorithm
Optimising Data Using K-Means Clustering Algorithm
Premeditated Initial Points for K-Means Clustering
Premeditated Initial Points for K-Means Clustering
"k-means-clustering" presentation @ Papers We Love Bucharest
"k-means-clustering" presentation @ Papers We Love Bucharest
Data Mining: Concepts and techniques: Chapter 11,Review: Basic Cluster Analys...
Data Mining: Concepts and techniques: Chapter 11,Review: Basic Cluster Analys...
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可查真实(Monash毕业证)西澳大学毕业证成绩单退学买
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一整套留学文凭证件服务：
一：西澳大学西澳大学毕业证成绩单毕业证 #成绩单等全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站永久可查
四：留信认证留学生信息网站永久可查
国外毕业证学位证成绩单办理方法：
1客户提供办理西澳大学西澳大学毕业证成绩单信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
教育部文凭学历认证认证的用途：
如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！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帮您快速整合材料让您少走弯路。
实体公司专业为您服务如有需要请联系我: 微信176555708
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◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微信：176555708】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
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→ 【关于价格问题（保证一手价格）
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
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部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！
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。
学历顾问：微信：176555708
留学学历(UoA毕业证)奥克兰大学毕业证成绩单官方原版办理
制做办理奥克兰大学学历证书＜176555708微信＞【毕业证明信-推荐信做学费单＞【微信176555708】【制作UoA毕业证文凭认证奥克兰大学毕业证成绩单购买】【UoA毕业证书】{奥克兰大学文凭购买}】成绩单，录取通知书，Offer，在读证明，雅思托福成绩单，真实大使馆教育部认证，回国人员证明，＞【制作UoA毕业证文凭认证奥克兰大学毕业证成绩单购买】【UoA毕业证书】{奥克兰大学文凭购买}留信网认证。
奥克兰大学学历证书＜微信176555708（一对一服务包括毕业院长签字,专业课程,学位类型,专业或教育领域,以及毕业日期.不要忽视这些细节.这两份文件同样重要!毕业证成绩单文凭留信网学历认证！）[留学文凭学历认证(留信认证使馆认证)奥克兰大学毕业证成绩单毕业证证书大学Offer请假条成绩单语言证书国际回国人员证明高仿教育部认证申请学校等一切高仿或者真实可查认证服务。
多年留学服务公司,拥有海外样板无数能完美1:1还原海外各国大学degreeDiplomaTranscripts等毕业材料。海外大学毕业材料都有哪些工艺呢？工艺难度主要由：烫金.钢印.底纹.水印.防伪光标.热敏防伪等等组成。而且我们每天都在更新海外文凭的样板以求所有同学都能享受到完美的品质服务。
国外毕业证学位证成绩单办理方法：
1客户提供办理奥克兰大学奥克兰大学硕士毕业证成绩单信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
— — — — 我们是挂科和未毕业同学们的福音我们是实体公司精益求精的工艺！ — — — -
一真实留信认证的作用(私企外企荣誉的见证):
1：该专业认证可证明留学生真实留学身份同时对留学生所学专业等级给予评定。
2：国家专业人才认证中心颁发入库证书这个入网证书并且可以归档到地方。
3：凡是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部网内查询个人身份证信息后同步读取人才网入库信息。
4：个人职称评审加20分个人信誉贷款加10分。
5：在国家人才网主办的全国网络招聘大会中纳入资料供国家500强等高端企业选择人才。
Gen Z and the marketplaces - let's translate their needs
The product workshop focused on exploring the requirements of Generation Z in relation to marketplace dynamics. We delved into their specific needs, examined the specifics in their shopping preferences, and analyzed their preferred methods for accessing information and making purchases within a marketplace. Through the study of real-life cases , we tried to gain valuable insights into enhancing the marketplace experience for Generation Z.
The workshop was held on the DMA Conference in Vienna June 2024.
一比一原版(SLU毕业证)圣路易斯大学毕业证成绩单专业办理
SLU毕业证原版定制【微信：176555708】【圣路易斯大学毕业证成绩单-学位证】【微信：176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微信：176555708】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
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→ 【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。
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
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部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！
学历顾问：微信：176555708
存档可查的(USC毕业证)南加利福尼亚大学毕业证成绩单制做办理
精仿办理南加利福尼亚大学毕业证成绩单（【微信176555708】）毕业证学历认证OFFER专卖国外文凭学历学位证书办理澳洲文凭|澳洲毕业证,澳洲学历认证,澳洲成绩单【微信176555708】 澳洲offer,教育部学历认证及使馆认证永久可查 ,国外毕业证|国外学历认证,国外学历文凭证书 USC毕业证，USC毕业证，USC毕业证，USC毕业证，USC毕业证，USC毕业证【微信176555708】，USC毕业证，专业为留学生办理毕业证、成绩单、使馆留学回国人员证明、教育部学历学位认证、录取通知书、Offer、（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微信：176555708】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
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→ 【关于价格问题（保证一手价格）
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
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部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！
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。
学历顾问：微信：176555708
留学挂科(UofM毕业证)明尼苏达大学毕业证成绩单复刻办理
精仿办理明尼苏达大学学历证书＜176555708微信＞【毕业证明信-推荐信做学费单＞【微信176555708】【制作UofM毕业证文凭认证明尼苏达大学毕业证成绩单购买】【UofM毕业证书】{明尼苏达大学文凭购买}】成绩单，录取通知书，Offer，在读证明，雅思托福成绩单，真实大使馆教育部认证，回国人员证明，＞【制作UofM毕业证文凭认证明尼苏达大学毕业证成绩单购买】【UofM毕业证书】{明尼苏达大学文凭购买}留信网认证。
明尼苏达大学学历证书＜微信176555708（一对一服务包括毕业院长签字,专业课程,学位类型,专业或教育领域,以及毕业日期.不要忽视这些细节.这两份文件同样重要!毕业证成绩单文凭留信网学历认证！）（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微信：176555708】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
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→ 【关于价格问题（保证一手价格）
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
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部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！
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。
学历顾问：微信：176555708
国外证书(Lincoln毕业证)新西兰林肯大学毕业证成绩单不能毕业办理
退学办理新西兰林肯大学毕业证【微信176555708】学历认证怎么做：原版仿制新西兰林肯大学电子版成绩单毕业证认证【新西兰林肯大学毕业证成绩单】、新西兰林肯大学文凭证书成绩单复刻offer录取通知书、购买Lincoln圣力嘉学院本科毕业证、【新西兰林肯大学毕业证办理Lincoln毕业证书哪里买】、新西兰林肯大学 Offer在线办理Lincoln Offer新西兰林肯大学Bachloer Degree。1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信176555708我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：新西兰林肯大学新西兰林肯大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信176555708）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。
成绩单ps(UST毕业证)圣托马斯大学毕业证成绩单快速办理
一比一原版制作UST毕业证【微信176555708】圣托马斯大学毕业证书原版↑制作圣托马斯大学学历认证文凭办理圣托马斯大学留信网认证,留学回国办理毕业证成绩单文凭学历认证【微信176555708】专业为海外学子办理毕业证成绩单、文凭制作，学历仿制，回国人员证明、做文凭，研究生、本科、硕士学历认证、留信认证、结业证、学位证书样本、美国教育部认证百分百真实存档可查】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微信：176555708】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
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→ 【关于价格问题（保证一手价格）
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
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部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！
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。
学历顾问：微信：176555708
假文凭国外(Adelaide毕业证)澳大利亚国立大学毕业证成绩单办理
购买澳大利亚国立大学毕业证>【微信176555708】办理澳大利亚国立大学毕业证成绩单【微信176555708】Adelaide毕业证成绩单Adelaide学历证书Adelaide文凭【Adelaide毕业套号文凭网认证澳大利亚国立大学毕业证成绩单】【哪里买澳大利亚国立大学毕业证文凭Adelaide成绩学校快递邮寄信封】【开版澳大利亚国立大学文凭】Adelaide留信认证本科硕士学历认证(诚招代理)微信：176555708办理国外高校毕业证成绩单文凭学位证,真实使馆公证（留学回国人员证明）真实留信网认证国外学历学位认证雅思代考国外学校代申请名校保录开请假条改GPA改成绩ID卡
1.高仿业务:【本科硕士】毕业证,成绩单（GPA修改）,学历认证（教育部认证）,大学Offer,,ID,留信认证,使馆认证,雅思,语言证书等高仿类证书；
2.认证服务: 学历认证（教育部认证）,大使馆认证（回国人员证明）,留信认证（可查有编号证书）,大学保录取,雅思保分成绩单。
3.技术服务：钢印水印烫金激光防伪凹凸版设计印刷激凸温感光标底纹镭射速度快。
办理澳大利亚国立大学澳大利亚国立大学毕业证成绩单流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
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Internet of Things in Manufacturing: Revolutionizing Efficiency & Quality | C...
This article delves into how the Internet of Things in manufacturing is reshaping the industry, its benefits, challenges, and future prospects.
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专业服务，请勿犹豫联系我！本公司是留学创业和海归创业者们的桥梁。一次办理，终生受用，一步到位，高效服务。详情请在线咨询办理,欢迎有诚意办理的客户咨询!洽谈。
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没文凭怎么找工作。让您回国发展信心十足！
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Should Repositories Participate in the Fediverse?
Presentation for OR2024 making the case that repositories could play a part in the "fediverse" of distributed social applications
制作毕业证书(ANU毕业证)莫纳什大学毕业证成绩单官方原版办理
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二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站永久可查
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1客户提供办理莫纳什大学莫纳什大学毕业证文凭证书信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
教育部文凭学历认证认证的用途：
如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！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帮您快速整合材料让您少走弯路。
实体公司专业为您服务如有需要请联系我: 微信176555708
办理新西兰奥克兰大学毕业证学位证书范本原版一模一样
原版一模一样【微信：741003700 】【新西兰奥克兰大学毕业证学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
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
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K means
- 2. K-Means for Social Application (Movie Contents) Opportunities
- 3. K-means法のアルゴリズム Step 1：K個のクラスターの中心をランダムに設定する。 Step 2：それぞれの個体を最も近い中心に割り当てる。 Step 3：クラスターごとに中心を計算しなおす。 すべてのクラスター中心が変化しなければ終了。 それ以外はStep 2へ戻る。
- 4. pseudo code for k-means complexity O(k*n/p) where k is number of clusters, n is number of data points and p is number of machines kmeans(data) { initial_centroids =pick(k, data) upload(data) writeToS3(initial_centroids) old_centroids = initial_centroids while (true){ map_reduce() new_centroids = readFromS3() or readFromAzureBlob() if change(new_centroids, old_centroids)< delta { break } else { old_centroids = new_centroids } } result = readFromS3() or readFromAzureBlob() return result }
- 5. SQL クエリでK-Means SELECT @iteration+1, cluster_id, AVERAGE(d.data) as center FROM (SELECT d.data, cc.cluster_id, ROW_NUMBER() OVER (PARTITION BY d.data ORDER BY DISTANCE(d.data, cc.center) as ranking FROM data d CROSS JOIN (SELECT * FROM cluster_centers cc WHERE iteration = @iteration) cc ) a WHERE ranking = 1 GROUP BY cluster_id
- 6. R でK−Means url ranking node = ranking[,1] rank = ranking[,2] group > length(rank) [1] 281903 > length(node) [1] 281903 > length(group) [1] 281903 d cl K-means clustering with 3 clusters of sizes 20135, 20135, 20133
- 7. JavaでK-Means Mapper protected void map(ClusterCenter key, Vector value, Context context) throws IOException, InterruptedException { ClusterCenter nearest = null; double nearestDistance = Double.MAX_VALUE; for (ClusterCenter c : centers) { double dist = DistanceMeasurer.measureDistance(c, value); if (nearest == null) { nearest = c; nearestDistance = dist; } else { if (nearestDistance > dist) { nearest = c; nearestDistance = dist; } } } context.write(nearest, value); }
- 8. JavaでK-Means Reducer protected void reduce(ClusterCenter key, Iterable<vector> values,</vector> Context context) throws IOException, InterruptedException { Vector newCenter = new Vector(); List<vector> vectorList = </vector>new LinkedList<vector>();</vector> int vectorSize = key.getCenter().getVector().length; newCenter.setVector(new double[vectorSize]); for (Vector value : values) { vectorList.add(new Vector(value)); for (int i = 0; i < value.getVector().length; i++) { newCenter.getVector()[i] += value.getVector()[i]; } } for (int i = 0; i < newCenter.getVector().length; i++) { newCenter.getVector()[i] = newCenter.getVector()[i] / vectorList.size(); } ClusterCenter center = new ClusterCenter(newCenter); centers.add(center); for (Vector vector : vectorList) { context.write(center, vector); } if (center.converged(key)) context.getCounter(Counter.CONVERGED).increment(1); }