Types of clustering and different types of clustering algorithms
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The document discusses different types of clustering algorithms: 1. Hard clustering assigns each data point to one cluster, while soft clustering allows points to belong to multiple clusters. 2. Hierarchical clustering builds clusters hierarchically in a top-down or bottom-up approach, while flat clustering does not have a hierarchy. 3. Model-based clustering models data using statistical distributions to find the best fitting model. It then provides examples of specific clustering algorithms like K-Means, Fuzzy K-Means, Streaming K-Means, Spectral clustering, and Dirichlet clustering.
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12 งานนำสนอ cluster analysis
12 งานนำสนอ cluster analysiskhuwawa2513 การวิเคราะห์จัดกลุ่ม(Cluster Analysis)เป็นการวิเคราะห์เพื่อศึกษาว่าบุคคล (Cases) หรือสิ่งต่างๆ (Objects) จะสามารถนำมาจัดกลุ่มกันตามความเหมือน (Similarity) หรือความแตกต่าง (Dissimilarity or Distance) ของตัวแปร (Variables) ได้กี่กลุ่ม อย่างไรบ้างบุคคลหรือสิ่งที่มีความคล้ายคลึงกันในตัวแปร
ที่นำมาวิเคราะห์จะถูกจัดอยู่ในกลุ่ม (Cluster) เดียวกัน ส่วนบุคคลหรือสิ่งที่แตกต่างกันในตัวแปรจะถูกจัดอยู่คนละกลุ่มกันมีหลายเทคนิคการวิเคราะห์ จัดกลุ่มโดยพยายามให้สิ่งที่อยู่ในกลุ่มเดียวกันมีความคล้ายคลึงกันมากที่สุด (Minimize Intra-Cluster Distances) และพยายามให้แต่ละกลุ่มมีความแตกต่างกันมากที่สุด (Maximize Inter-Cluster Distances)กำหนดตัวแปรที่ใช้ในการจัดกลุ่มตัวแปรต้องมีความสำคัญและมีความหมายผลการวิเคราะห์จะดีหรือไม่ขึ้นอยู่กับตัวแปรที่ใช้จัดกลุ่ม ตัวแปรใดที่ไม่เกี่ยวข้องควรตัดออกแล้ววิเคราะห์ใหม่เลือกมาตรวัดระยะห่าง (Similarity or Distance)การเลือกมาตรวัดที่ต่างกัน
มีผลทำให้ผลวิเคราะห์แตกต่างกันได้
ดังนั้นจึงควรลองใช้หลายมาตรวัดและเปรียบเทียบผลถ้าตัวแปรมีมาตรวัดคะแนนต่างกัน
ควรแปลงเป็นคะแนนมาตรฐานก่อนการวิเคราะห์เลือกวิธีการจัดกลุ่มHierarchical Clustering , Partitional Clustering or K-Means Clustering , Two-Step Clusteringตัดสินใจว่าจะจัดกลุ่มกี่กลุ่ม
แปลความหมายของกลุ่มที่จัดทดสอบความเที่ยงตรงของกลุ่ม วิเคราะห์ Profile ของกลุ่ม
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12 งานนำสนอ cluster analysis
12 งานนำสนอ cluster analysiskhuwawa2513 การวิเคราะห์จัดกลุ่ม(Cluster Analysis)เป็นการวิเคราะห์เพื่อศึกษาว่าบุคคล (Cases) หรือสิ่งต่างๆ (Objects) จะสามารถนำมาจัดกลุ่มกันตามความเหมือน (Similarity) หรือความแตกต่าง (Dissimilarity or Distance) ของตัวแปร (Variables) ได้กี่กลุ่ม อย่างไรบ้างบุคคลหรือสิ่งที่มีความคล้ายคลึงกันในตัวแปร
ที่นำมาวิเคราะห์จะถูกจัดอยู่ในกลุ่ม (Cluster) เดียวกัน ส่วนบุคคลหรือสิ่งที่แตกต่างกันในตัวแปรจะถูกจัดอยู่คนละกลุ่มกันมีหลายเทคนิคการวิเคราะห์ จัดกลุ่มโดยพยายามให้สิ่งที่อยู่ในกลุ่มเดียวกันมีความคล้ายคลึงกันมากที่สุด (Minimize Intra-Cluster Distances) และพยายามให้แต่ละกลุ่มมีความแตกต่างกันมากที่สุด (Maximize Inter-Cluster Distances)กำหนดตัวแปรที่ใช้ในการจัดกลุ่มตัวแปรต้องมีความสำคัญและมีความหมายผลการวิเคราะห์จะดีหรือไม่ขึ้นอยู่กับตัวแปรที่ใช้จัดกลุ่ม ตัวแปรใดที่ไม่เกี่ยวข้องควรตัดออกแล้ววิเคราะห์ใหม่เลือกมาตรวัดระยะห่าง (Similarity or Distance)การเลือกมาตรวัดที่ต่างกัน
มีผลทำให้ผลวิเคราะห์แตกต่างกันได้
ดังนั้นจึงควรลองใช้หลายมาตรวัดและเปรียบเทียบผลถ้าตัวแปรมีมาตรวัดคะแนนต่างกัน
ควรแปลงเป็นคะแนนมาตรฐานก่อนการวิเคราะห์เลือกวิธีการจัดกลุ่มHierarchical Clustering , Partitional Clustering or K-Means Clustering , Two-Step Clusteringตัดสินใจว่าจะจัดกลุ่มกี่กลุ่ม
แปลความหมายของกลุ่มที่จัดทดสอบความเที่ยงตรงของกลุ่ม วิเคราะห์ Profile ของกลุ่ม
Cluster analysis
Cluster analysissaba khan Cluster analysis is a descriptive technique that groups similar objects into clusters. It finds natural groupings within data according to characteristics in the data. Cluster analysis is used for taxonomy development, data simplification, and relationship identification. Some applications of cluster analysis include market segmentation in marketing, grouping users on social networks, and reducing markers on maps. It requires representative data and assumes groups will be sufficiently sized and not distorted by outliers.
Belief Networks & Bayesian Classification
Belief Networks & Bayesian ClassificationAdnan Masood This document provides an introduction to Bayesian belief networks and naive Bayesian classification. It defines key probability concepts like joint probability, conditional probability, and Bayes' rule. It explains how Bayesian belief networks can represent dependencies between variables and how naive Bayesian classification assumes conditional independence between variables. The document concludes with examples of how to calculate probabilities and classify new examples using a naive Bayesian approach.
Association Analysis
Association Analysisguest0edcaf Association analysis is used to uncover relationships between data items by identifying frequent patterns and association rules. The Apriori algorithm is a two-step process used for association rule mining: 1) find frequent itemsets that satisfy a minimum support threshold, and 2) generate strong association rules from the frequent itemsets that meet minimum support and confidence thresholds. Practical issues like level of data aggregation and appropriate support/confidence levels must be considered.
Chap8 basic cluster_analysis
Chap8 basic cluster_analysisguru_prasadg - Hierarchical clustering produces nested clusters organized as a hierarchical tree called a dendrogram. It can be either agglomerative, where each point starts in its own cluster and clusters are merged, or divisive, where all points start in one cluster which is recursively split.
- 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.
- K-means is a partitional clustering algorithm that divides data into k non-overlapping clusters based on minimizing distance between points and cluster centroids. It is fast but sensitive to initialization and assumes spherical clusters of similar size and density.
Data Mining: clustering and analysis
Data Mining: clustering and analysisDataminingTools Inc Clustering is the process of grouping similar objects together. It allows data to be analyzed and summarized. There are several methods of clustering including partitioning, hierarchical, density-based, grid-based, and model-based. Hierarchical clustering methods are either agglomerative (bottom-up) or divisive (top-down). Density-based methods like DBSCAN and OPTICS identify clusters based on density. Grid-based methods impose grids on data to find dense regions. Model-based clustering uses models like expectation-maximization. High-dimensional data can be clustered using subspace or dimension-reduction methods. Constraint-based clustering allows users to specify preferences.
Clustering: A Survey
Clustering: A SurveyRaffaele Capaldo 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.
Bayesian Networks - A Brief Introduction
Bayesian Networks - A Brief IntroductionAdnan Masood - A Bayesian network is a graphical model that depicts probabilistic relationships among variables. It represents a joint probability distribution over variables in a directed acyclic graph with conditional probability tables.
- A Bayesian network consists of a directed acyclic graph whose nodes represent variables and edges represent probabilistic dependencies, along with conditional probability distributions that quantify the relationships.
- Inference using a Bayesian network allows computing probabilities like P(X|evidence) by taking into account the graph structure and probability tables.
Bayesian Belief Networks for dummies
Bayesian Belief Networks for dummiesGilad Barkan The document discusses Bayesian Belief Networks (BBNs), which are probabilistic graphical models used for reasoning under uncertainty, particularly in contexts such as weather prediction. It explains how BBNs use nodes to represent random variables and edges to denote causal relationships, along with the role of conditional probability tables (CPTs) in determining the probabilities of various outcomes. Furthermore, it highlights the advantages of BBNs in modeling complex relationships and estimating joint probabilities efficiently, addressing the curse of dimensionality in probabilistic reasoning.
Clustering training
Clustering trainingGabor Veress The document outlines the principles and methods of clustering, emphasizing the grouping of similar objects while ensuring dissimilarity between clusters. It covers various clustering techniques, distance measures, and their applications across fields such as marketing, banking, and image processing. Additionally, the document discusses key requirements and considerations for effective clustering analysis.
K means Clustering
K means ClusteringEdureka! 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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15857 cse422 unsupervised-learning
15857 cse422 unsupervised-learningAnil Yadav This document provides an overview of supervised and unsupervised learning, with a focus on clustering as an unsupervised learning technique. It describes the basic concepts of clustering, including how clustering groups similar data points together without labeled categories. It then covers two main clustering algorithms - k-means, a partitional clustering method, and hierarchical clustering. It discusses aspects like cluster representation, distance functions, strengths and weaknesses of different approaches. The document aims to introduce clustering and compare it with supervised learning.
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Chapter 11 cluster advanced, Han & KamberHouw Liong The This document summarizes various clustering algorithms including:
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Chapter 11 cluster advanced : web and text miningHouw Liong The This document provides an overview of advanced clustering analysis techniques discussed in Chapter 11 of the textbook "Data Mining: Concepts and Techniques". It begins with an introduction to probability model-based clustering and fuzzy clustering. It then discusses using the EM algorithm for fuzzy clustering and fitting univariate Gaussian mixture models. Next, it covers challenges with clustering high-dimensional data and methods for subspace clustering. It also briefly introduces clustering graphs and network data as well as clustering with constraints. The document concludes with an outline of the chapter.
Introduction to Clustering . pptx
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Dp33701704IJERA Editor The document discusses a novel fuzzy clustering approach used in data mining, which focuses on grouping similar data points while allowing for degrees of membership across multiple clusters. It reviews various clustering algorithms and proposes a method that leverages fuzzy set theory to enhance cluster accuracy by using fuzzy regions and frequent itemset analysis. The approach culminates in the creation of term cluster and data cluster matrices for effective data partitioning.
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Dp33701704IJERA Editor The document discusses a fuzzy clustering approach in data mining, focusing on the technique's ability to group similar data points based on their characteristics. It explains various clustering methods, particularly fuzzy clustering, which allows for partial membership in multiple clusters, enhancing the model's flexibility. The proposed algorithm aims to optimize cluster formation by utilizing fuzzy membership functions to analyze numerical datasets.
A survey on Efficient Enhanced K-Means Clustering Algorithm
A survey on Efficient Enhanced K-Means Clustering Algorithmijsrd.com This document presents a survey on the efficient enhanced k-means clustering algorithm, highlighting its importance in data mining for pattern recognition and classification. It discusses various clustering methods, compares different k-means variants, and emphasizes the algorithm's simplicity, flexibility, and associated challenges, such as the dependency on initial centroids. Future work includes developing new centroid initialization algorithms and comparing their performance against standard k-means.
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DS9 - Clustering.pptxJK970901 This document provides an overview of clustering analysis techniques, including k-means clustering, DBSCAN clustering, and self-organizing maps (SOM). It defines clustering as the process of grouping similar data points together. K-means clustering partitions data into k clusters by finding cluster centroids. DBSCAN identifies clusters based on density rather than specifying the number of clusters. SOM projects high-dimensional data onto a low-dimensional grid using a neural network approach.
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Clustering & classificationJamshed Khan This document provides an overview of clustering and classification techniques in data mining. It defines clustering and classification as unsupervised and supervised learning respectively. The document discusses how classification works by building a model from training data and then using the model to classify new data. For clustering, it explains that clusters are formed by grouping similar data objects without predefined labels. The document also describes different types of clustering techniques like hierarchical, partitioning, and probabilistic clustering. Finally, it provides a step-by-step explanation of the k-means clustering algorithm.
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Clustering[306] [Read-Only].pdfigeabroad Clustering is an unsupervised machine learning technique that groups unlabeled data points into clusters based on similarities. It partitions data into meaningful subgroups without predefined labels. Common clustering algorithms include k-means, hierarchical, density-based, and grid-based methods. K-means clustering aims to partition data into k clusters where each data point belongs to the cluster with the nearest mean. It is sensitive to outliers but simple and fast.
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Types of clustering and different types of clustering algorithms
- 1. Types of clustering: Clustering can be divided into different categories based on different criteria • 1.Hard clustering: A given data point in n-dimensional space only belongs to one cluster. This is also known as exclusive clustering. The K-Means clustering mechanism is an example of hard clustering. • 2.Soft clustering: A given data point can belong to more than one cluster in soft clustering. This is also known as overlapping clustering. The Fuzzy K-Means algorithm is a good example of soft clustering. • 3.Hierarchial clustering: In hierarchical clustering, a hierarchy of clusters is built using the top-down (divisive) or bottom-up (agglomerative) approach. • 4. Flat clustering: Is a simple technique where no hierarchy is present. • 5.Model-based clustering: In model-based clustering, data is modeled using a standard statistical model to work with different distributions. The idea is to find a model that best fits the data.
- 2. Different clustering algorithms • Fuzzy K-Means: • The K-Means algorithm is for hard clustering. In hard clustering, one data point belongs only to one cluster. However, there can be situations where one point belongs to more than one cluster. For example, a news article may belong to both the Technology and Current Affairs categories. In that case, we need a soft clustering mechanism. • The Fuzzy K-Means algorithm implements soft clustering. It generates overlapping clusters. Each point has a probability of belonging to each cluster, based on the distance from each centroid. • In this example, we apply the Fuzzy K-Means algorithm for dataset(22 80 ,25 75 ,28 85 ,55 150,50 145 ,53 153 ,38 115 ) The outcome of the example is given in the following figure. Note that the newly added data point (someone who had medium weight and height) belongs to cluster 3 in 0.52 probability and to cluster 1 in 0.47 probability, whereas other data points (people who are either large or small) belongs to nearly 0.9 to a particular cluster.
- 3. Streaming K-Means • If the volume of data is too large to be stored in the main memory available, the K-Means algorithm is not suitable, as it's batch processing mechanism iterates over all the data points. Also, the K-Means algorithm is sensitive to the noise and outliers in data. • Streaming K-Means algorithms has provided a solution for these problems by operating in two steps, as follows: • The streaming step • The ball K-Means step • The idea is to read data points sequentially, storing very few data points in memory. • Then, after the first step, a better representative set of weighted data points is produced for further processing. • The final K number of clusters is produced in the ball K-Means step. During the second step, potential outliers are eliminated.
- 4. Spectral clustering • The spectral clustering algorithm is helpful in hard, nonconvex clustering problems. It clusters points using the eigenvectors of matrices derived from data.
- 5. Dirichlet clustering • The Fuzzy K-Means and K-Means algorithms model clusters as spheres (circles in n-dimensional space.) K-Means assumes a common fixed variance. Further, K-Means does not model the data point distribution. • A normal data distribution should be there for the K-Means and Fuzzy K-Means algorithms to process effectively. If the data distribution is different, for example, an asymmetrical normal distribution (different standard deviations), the K-Means algorithm will not perform well and will not give good results. • Dirichlet clustering can be applied to model different data distributions (data points that are not in normal distribution) effectively. Dirichlet clustering fits a model over a dataset and tunes parameters to adjust the model's parameters to correctly fit the data. This approach is suitable to address the hierarchical-clustering problem.