Overview of the state-of-the-art Time Series Clustering based on literature study; distance metrics, prototypes, time-series preprocessing, and clustering algorithms
This document provides an overview of graph neural networks (GNNs). GNNs are a type of neural network that can operate on graph-structured data like molecules or social networks. GNNs learn representations of nodes by propagating information between connected nodes over many layers. They are useful when relationships between objects are important. Examples of applications include predicting drug properties from molecular graphs and program understanding by modeling code as graphs. The document explains how GNNs differ from RNNs and provides examples of GNN variations, datasets, and frameworks.
This document provides an agenda for a presentation on deep learning, neural networks, convolutional neural networks, and interesting applications. The presentation will include introductions to deep learning and how it differs from traditional machine learning by learning feature representations from data. It will cover the history of neural networks and breakthroughs that enabled training of deeper models. Convolutional neural network architectures will be overviewed, including convolutional, pooling, and dense layers. Applications like recommendation systems, natural language processing, and computer vision will also be discussed. There will be a question and answer section.
An overview of gradient descent optimization algorithms Hakky St
This document provides an overview of various gradient descent optimization algorithms that are commonly used for training deep learning models. It begins with an introduction to gradient descent and its variants, including batch gradient descent, stochastic gradient descent (SGD), and mini-batch gradient descent. It then discusses challenges with these algorithms, such as choosing the learning rate. The document proceeds to explain popular optimization algorithms used to address these challenges, including momentum, Nesterov accelerated gradient, Adagrad, Adadelta, RMSprop, and Adam. It provides visualizations and intuitive explanations of how these algorithms work. Finally, it discusses strategies for parallelizing and optimizing SGD and concludes with a comparison of optimization algorithms.
The fourth lecture from the Machine Learning course series of lectures. This lecture first introduces a problem of visualising multi-dimensional data on fewer dimensions and later discusses one of the most popular methods for reducing dimensionality - principal component analysis (PCA). Later, also t-SNE is mentioned briefly as a non-linear alternative to PCA. A link to my github (https://github.com/skyfallen/MachineLearningPracticals) with practicals that I have designed for this course in both R and Python. I can share keynote files, contact me via e-mail: dmytro.fishman@ut.ee.
The document discusses machine learning classification using the MNIST dataset of handwritten digits. It begins by defining classification and providing examples. It then describes the MNIST dataset and how it is fetched in scikit-learn. The document outlines the steps of classification which include dividing the data into training and test sets, training a classifier on the training set, testing it on the test set, and evaluating performance. It specifically trains a stochastic gradient descent (SGD) classifier on the MNIST data. The performance is evaluated using cross validation accuracy, confusion matrix, and metrics like precision and recall.
https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Generative adversarial networks (GANs) are a class of machine learning frameworks where two neural networks, a generator and discriminator, compete against each other. The generator learns to generate new data with the same statistics as the training set to fool the discriminator, while the discriminator learns to better distinguish real samples from generated samples. GANs have applications in image generation, image translation between domains, and image completion. Training GANs can be challenging due to issues like mode collapse.
This document discusses machine learning and various applications of machine learning. It provides an introduction to machine learning, describing how machine learning programs can automatically improve with experience. It discusses several successful machine learning applications and outlines the goals and multidisciplinary nature of the machine learning field. The document also provides examples of specific machine learning achievements in areas like speech recognition, credit card fraud detection, and game playing.
The document discusses Generative Adversarial Networks (GANs), a type of generative model proposed by Ian Goodfellow in 2014. GANs use two neural networks, a generator and discriminator, that compete against each other. The generator produces synthetic data to fool the discriminator, while the discriminator learns to distinguish real from synthetic data. GANs have been used successfully to generate realistic images when trained on large datasets. Examples mentioned include Pix2Pix for image-to-image translation and STACKGAN for text-to-image generation.
This document discusses unsupervised learning and clustering. It defines unsupervised learning as modeling the underlying structure or distribution of input data without corresponding output variables. Clustering is described as organizing unlabeled data into groups of similar items called clusters. The document focuses on k-means clustering, describing it as a method that partitions data into k clusters by minimizing distances between points and cluster centers. It provides details on the k-means algorithm and gives examples of its steps. Strengths and weaknesses of k-means clustering are also summarized.
GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production.
Starting from the very basic of what a GAN is, passing trough Tensorflow implementation, using the most cutting-edge APIs available in the framework, and finally, production-ready serving at scale using Google Cloud ML Engine.
Slides for the talk: https://www.pycon.it/conference/talks/deep-diving-into-gans-form-theory-to-production
Github repo: https://github.com/zurutech/gans-from-theory-to-production
This slide first introduces the sequential pattern mining problem and also presents some required definitions in order to understand GSP algorithm. At then end there is a brief introduction of GSP algorithm and some practical constraints which it supports.
This Logistic Regression Presentation will help you understand how a Logistic Regression algorithm works in Machine Learning. In this tutorial video, you will learn what is Supervised Learning, what is Classification problem and some associated algorithms, what is Logistic Regression, how it works with simple examples, the maths behind Logistic Regression, how it is different from Linear Regression and Logistic Regression applications. At the end, you will also see an interesting demo in Python on how to predict the number present in an image using Logistic Regression.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. What is supervised learning?
2. What is classification? what are some of its solutions?
3. What is logistic regression?
4. Comparing linear and logistic regression
5. Logistic regression applications
6. Use case - Predicting the number in an image
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
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About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
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Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
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What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
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Deep learning techniques are increasingly being used for recommender systems. Neural network models such as word2vec, doc2vec and prod2vec learn embedding representations of items from user interaction data that capture their relationships. These embeddings can then be used to make recommendations by finding similar items. Deep collaborative filtering models apply neural networks to matrix factorization techniques to learn joint representations of users and items from rating data.
This document summarizes various optimization techniques for deep learning models, including gradient descent, stochastic gradient descent, and variants like momentum, Nesterov's accelerated gradient, AdaGrad, RMSProp, and Adam. It provides an overview of how each technique works and comparisons of their performance on image classification tasks using MNIST and CIFAR-10 datasets. The document concludes by encouraging attendees to try out the different optimization methods in Keras and provides resources for further deep learning topics.
Abstract: This PDSG workshop introduces basic concepts of splitting a dataset for training a model in machine learning. Concepts covered are training, test and validation data, serial and random splitting, data imbalance and k-fold cross validation.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Clustering algorithms are used to group similar data points together. K-means clustering aims to partition data into k clusters by minimizing distances between data points and cluster centers. Hierarchical clustering builds nested clusters by merging or splitting clusters based on distance metrics. Density-based clustering identifies clusters as areas of high density separated by areas of low density, like DBScan which uses parameters of minimum points and epsilon distance.
Learning a nonlinear embedding by preserving class neibourhood structure 최종WooSung Choi
Salakhutdinov, Ruslan, and Geoffrey E. Hinton. "Learning a nonlinear embedding by preserving class neighbourhood structure." International Conference on Artificial Intelligence and Statistics. 2007.
tIt appears that you've provided a set of instructions or input format for a machine learning task, particularly clustering using K-Means. Let's break down what each component means:
(number of clusters):
This is a placeholder for an actual numerical value that represents the desired number of clusters into which you want to divide your training data. In K-Means clustering, you need to specify in advance how many clusters (K) you want the algorithm to find in your data.
Training set:
The "training set" is your dataset, which contains the data points that you want to cluster. Each data point represents an observation or sample in your dataset.
(drop convention):
It's not clear from this input what "(drop convention)" refers to. It could be related to a specific data preprocessing or handling instruction, but without additional context or information, it's challenging to provide a precise explanation for this part.
In summary, you are expected to provide the number of clusters (K) that you want to discover in your training data, and the training data itself contains the observations or samples that will be used for clustering. The "(drop convention)" part may require further clarification or context to provide a meaningful explanation.Clustering is a fundamental concept in the field of machine learning and data analysis that involves grouping similar data points together based on certain criteria or patterns. It is a technique used to discover inherent structures, relationships, or similarities within a dataset when there are no predefined labels or categories. Clustering is widely employed in various domains, including marketing, biology, image analysis, recommendation systems, and more. In this comprehensive explanation of clustering, we will explore its principles, methods, applications, and key considerations.
Table of Contents
Introduction to Clustering
Key Concepts and Terminology
Types of Clustering
3.1. Partitioning Clustering
3.2. Hierarchical Clustering
3.3. Density-Based Clustering
3.4. Model-Based Clustering
Distance Metrics and Similarity Measures
Common Clustering Algorithms
5.1. K-Means Clustering
5.2. Hierarchical Agglomerative Clustering
5.3. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
5.4. Gaussian Mixture Models (GMM)
Evaluation of Clusters
Applications of Clustering
7.1. Customer Segmentation
7.2. Image Segmentation
7.3. Anomaly Detection
7.4. Document Clustering
7.5. Recommender Systems
7.6. Genomic Clustering
Challenges and Considerations
8.1. Determining the Number of Clusters (K)
8.2. Handling High-Dimensional Data
8.3. Initial Centroid Selection
8.4. Scaling and Normalization
8.5. Interpretation of Results
Best Practices in Clustering
Future Trends and Advances
Conclusion
1. Introduction to Clustering
Clustering, in the context of data analysis and machine learning, refers to the process of grouping a set of data points into subsets,
본 논문에서는 분배형 강화학습(Distributional Reinforcement Learning)에서 벨만 다이내믹스를 통해 확률 분포를 학습하는 문제를 고려합니다. 이전 연구들은 각 반환 분포의 유한 개의 통계량을 신경망을 통해 학습하는 방법을 사용해왔으나, 이 방법은 반환 분포의 함수적 형태에 제한을 받아 제한적인 표현력을 가지며, 미리 정의된 통계량을 유지하는 것이 어려웠습니다. 본 논문에서는 이러한 제한을 없애기 위해 최대 평균 거리(Maximum Mean Discrepancy, MMD)라는 가설 검정 기술을 활용해 반환 분포의 결정론적인(의사 난수를 사용한) 표본들을 학습하는 방법을 제안합니다. 이를 통해 반환 분포와 벨만 타겟 간의 모든 모멘트(순간값)를 암묵적으로 일치시킴으로써 분배형 벨만 연산자의 수렴성을 보장하며, 분포 근사에 대한 유한 샘플 분석을 제시합니다. 실험 결과, 본 논문에서 제안한 방법은 분배형 강화학습의 기본 모델보다 우수한 성능을 보이며, Atari 게임에서 분산형 에이전트를 사용하지 않는 경우에도 최고 성적을 기록합니다.
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.
This document provides an overview of machine learning techniques that can be applied in finance, including exploratory data analysis, clustering, classification, and regression methods. It discusses statistical learning approaches like data mining and modeling. For clustering, it describes techniques like k-means clustering, hierarchical clustering, Gaussian mixture models, and self-organizing maps. For classification, it mentions discriminant analysis, decision trees, neural networks, and support vector machines. It also provides summaries of regression, ensemble methods, and working with big data and distributed learning.
This document summarizes a talk given by Heiko Strathmann on using partial posterior paths to estimate expectations from large datasets without full posterior simulation. The key ideas are:
1. Construct a path of "partial posteriors" by sequentially adding mini-batches of data and computing expectations over these posteriors.
2. "Debias" the path of expectations to obtain an unbiased estimator of the true posterior expectation using a technique from stochastic optimization literature.
3. This approach allows estimating posterior expectations with sub-linear computational cost in the number of data points, without requiring full posterior simulation or imposing restrictions on the likelihood.
Experiments on synthetic and real-world examples demonstrate competitive performance versus standard M
This document summarizes a research paper that proposes a new method to accelerate the nearest neighbor search step of the k-means clustering algorithm. The k-means algorithm is computationally expensive due to calculating distances between data points and cluster centers. The proposed method uses geometric relationships between data points and centers to reject centers that are unlikely to be the nearest neighbor, without decreasing clustering accuracy. Experimental results showed the method significantly reduced the number of distance computations required.
We consider the problem of finding anomalies in high-dimensional data using popular PCA based anomaly scores. The naive algorithms for computing these scores explicitly compute the PCA of the covariance matrix which uses space quadratic in the dimensionality of the data. We give the first streaming algorithms
that use space that is linear or sublinear in the dimension. We prove general results showing that any sketch of a matrix that satisfies a certain operator norm guarantee can be used to approximate these scores. We instantiate these results with powerful matrix sketching techniques such as Frequent Directions and random projections to derive efficient and practical algorithms for these problems, which we validate over real-world data sets. Our main technical contribution is to prove matrix perturbation
inequalities for operators arising in the computation of these measures.
-Proceedings: https://arxiv.org/abs/1804.03065
-Origin: https://arxiv.org/abs/1804.03065
This document summarizes a distributed cloud-based genetic algorithm framework called TunUp for tuning the parameters of data clustering algorithms. TunUp integrates existing machine learning libraries and implements genetic algorithm techniques to tune parameters like K (number of clusters) and distance measures for K-means clustering. It evaluates internal clustering quality metrics on sample datasets and tunes parameters to optimize a chosen metric like AIC. The document outlines TunUp's features, describes how it implements genetic algorithms and parallelization, and concludes it is an open solution for clustering algorithm evaluation, validation and tuning.
Event classification & prediction using support vector machineRuta Kambli
This document provides an overview of event classification and prediction using support vector machines (SVM). It begins with an introduction to classification, machine learning, and SVM. It then discusses binary classification with SVM, including hard-margin and soft-margin SVM, kernels, and multiclass classification. The document presents case studies on classifying hand movements from electromyography data and predicting power grid blackouts using SVM. It concludes that SVM is effective for these classification tasks and can initiate prevention mechanisms for predicted events.
PyData NYC 2015 - Automatically Detecting Outliers with Datadog Datadog
Monitoring even a modestly-sized systems infrastructure quickly becomes untenable without automated alerting. For many metrics it is nontrivial to define ahead of time what constitutes “normal” versus “abnormal” values. This is especially true for metrics whose baseline value fluctuates over time. To make this problem more tractable, Datadog provides outlier detection functionality to automatically identify any host (or group of hosts) that is behaving abnormally compared to its peers.
These slides cover the algorithms we use for outlier detection, and show how easy they are to implement using Python. This presentation also covers the lessons we've learned from using outlier detection on our own systems, along with some real-life examples on how to avoid false positives and negatives.
Learn more at www.datadoghq.com.
This document discusses different types of clustering analysis techniques in data mining. It describes clustering as the task of grouping similar objects together. The document outlines several key clustering algorithms including k-means clustering and hierarchical clustering. It provides an example to illustrate how k-means clustering works by randomly selecting initial cluster centers and iteratively assigning data points to clusters and recomputing cluster centers until convergence. The document also discusses limitations of k-means and how hierarchical clustering builds nested clusters through sequential merging of clusters based on a similarity measure.
Hierarchical clustering is a method of partitioning a set of data into meaningful sub-classes or clusters. It involves two approaches - agglomerative, which successively links pairs of items or clusters, and divisive, which starts with the whole set as a cluster and divides it into smaller partitions. Agglomerative Nesting (AGNES) is an agglomerative technique that merges clusters with the least dissimilarity at each step, eventually combining all clusters. Divisive Analysis (DIANA) is the inverse, starting with all data in one cluster and splitting it until each data point is its own cluster. Both approaches can be visualized using dendrograms to show the hierarchical merging or splitting of clusters.
Locations are described with feature histograms based on surface orientation and smoothness, and loop closure can be detected by matching feature histograms.
Variable neighborhood Prediction of temporal collective profiles by Keun-Woo ...EuroIoTa
Temporal collective profiles generated by mobile network users can be used to predict network usage, which in turn can be used to improve the performance of the network to meet user demands. This presentation will talk about a prediction method of temporal collective profiles which is suitable for online network management. Using weighted graph representation, the target sample is observed during a given period to determine a set of neighboring profiles that are considered to behave similarly enough. The prediction of the target profile is based on the weighted average of its neighbors, where the optimal number of neighbors are selected through a form of variable neighborhood search. This method is applied to two datasets, one provided by a mobile network service provider and the other from a Wi-Fi service provider. The proposed prediction method can conveniently characterize user behavior via graph representation, while outperforming existing prediction methods. Also, unlike existing methods that utilize categorization, it has a low computational complexity, which makes it suitable for online network analysis.
Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...MLconf
Graph Representation Learning with Deep Embedding Approach:
Graphs are commonly used data structure for representing the real-world relationships, e.g., molecular structure, knowledge graphs, social and communication networks. The effective encoding of graphical information is essential to the success of such applications. In this talk I’ll first describe a general deep learning framework, namely structure2vec, for end to end graph feature representation learning. Then I’ll present the direct application of this model on graph problems on different scales, including community detection and molecule graph classification/regression. We then extend the embedding idea to temporal evolving user-product interaction graph for recommendation. Finally I’ll present our latest work on leveraging the reinforcement learning technique for graph combinatorial optimization, including vertex cover problem for social influence maximization and traveling salesman problem for scheduling management.
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4. Background
• High dimensionality
• Irregular lengths
• Noise and time shifts
time (s)
variable
A time series is a collection of observations made sequentially in time.
15. Which Distance Measure to Use
• Type of the data
• Research questions
Criteria Euclidean DTW
Supports Time Series length differences No Yes
Supports Time Series time shifts No Yes
Computational costs Low High
29. Clustering
Clustering algorithm Distance measure Prototype
Partitional
K – means / K – medoid Euclidean / Manhattan Mean / PAM
TAD Pole DTW DBA
K – shape SBD Shape Extraction
Hierarchical Agglomerative All All
Clustering AlgorithmDistance
Measure
Prototype
N clusters
Time Series
Data
44. DTW
Right combination of distance measure & prototype
Conclusions
Clustering algorithm Distance measure Prototype
Partitional
K – means / K – medoid Euclidean / Manhattan Mean / PAM
TAD Pole DTW DBA
K – shape SBD Shape Extraction
Hierarchical Agglomerative All All
Editor's Notes
Provide quantification for the dissimilarity between two time-series
The classification of objects, into clusters, requires some methods for measuring the distance or the (dis)similarity between the objects
The term proximity is used to refer to either similarity or dissimilarity. Frequently, the term distance is used as a synonym for dissimilarity.
Variable for
Recent years have seen a surge of interest in time series clustering.
Data characteristics are evolving and traditional clustering algorithms are becoming less popular in time series clustering.
The most commonly used distance measures are only defined for series of equal length and are sensitive to noise, scale and time shifts
Thus, many other distance measures tailored to time-series have been developed in order to overcome these limitations; other challenges associated with the structure of time-series, such as multiple variables, serial correlation
each
Goal is to put them all together in clusters
Input in customer segmentation
Mention about chicken segmentation
Behavior based on purchases, bank transactions, energy, other utilities usage/consumption, social networks – who is connected to who
Hierarchy of classes dendrogram
Provide quantification for the dissimilarity between two time-series
The classification of objects, into clusters, requires some methods for measuring the distance or the (dis)similarity between the objects
The term proximity is used to refer to either similarity or dissimilarity. Frequently, the term distance is used as a synonym for dissimilarity.
https://en.wikipedia.org/wiki/Taxicab_geometry
The distance between two points measured along axes at right angles.
Also known as Manhattan length, rectilinear distance, Minkowski's L1 distance, L1 norm, taxi cab metric, snake distance, city block distance
Correlation measures are only useful if/when the relationship between attributes is linear. So if the correlation is 0, then there is no linear relationship between the two data objects.
http://cs.tsu.edu/ghemri/CS497/ClassNotes/ML/Similarity%20Measures.pdf
Be ready to explain pearson and spearman
When time series have different lengths
One of the most used measure of the similarity between two time series
Originally designed to treat automatic speech recognition
Optimal global alignment between two time series, exploiting temporal distortions between them
Designed especially for time series analysis
Ignore shifts in time dimension
Ignore speeds of two time series
How is it calculated?
When time series have different lengths
One of the most used measure of the similarity between two time series
Originally designed to treat automatic speech recognition
Optimal global alignment between two time series, exploiting temporal distortions between them
Designed especially for time series analysis
Ignore shifts in time dimension
Ignore speeds of two time series
How is it calculated?
https://www.datanovia.com/en/lessons/clustering-distance-measures/
For example, correlation-based distance is often used in gene expression data analysis.
Correlation-based distance considers two objects to be similar if their features are highly correlated, even though the observed values may be far apart in terms of Euclidean distance.
For most clustering package, Euclidean is default.
If we want to identify clusters of observations with the same overall profiles regardless of their magnitudes, then correlation-based distance
If correlation, Pearson’s correlation is quite sensitive to outliers
Commonly used in
gene expression data analysis
marketing, if we want to identify group of shoppers with the same preference in term of items, regardless of the volume of items they bought.
Hierarchy of classes dendrogram
Gamma is the optimization function.
A is the alignment function
Hierarchy of classes dendrogram
Hierarchy of classes dendrogram
Clusters are defines beforehand
Compute distance between point and centroids and keep the minimum
Predict For each data point calculate the distance from both centroids and the data point is assigned to the cluster with the min distance
Move centroids in the point where the is the mean distance so that they are in the center of the cluster
Compute distance between point and centroids and keep the minimum
Predict For each data point calculate the distance from both centroids and the data point is assigned to the cluster with the min distance
Move centroids in the point where the is the mean distance so that they are in the center of the cluster
Compute distance between point and centroids and keep the minimum
Predict For each data point calculate the distance from both centroids and the data point is assigned to the cluster with the min distance
Move centroids in the point where the is the mean distance so that they are in the center of the cluster
Hierarchy of classes dendrogram
Each character has each one cluster
Input = genetic code
Selma + Patty twins
Lisa + Merge mother and daughter (less similarity because the share genetic code with Homer Simpson)
Selma + patty sisters of Marge
Number of clusters and order of clustering
A: number of time series assigned to same cluster and belong to the same class
B: number of time series assigned to different cluster and belong to the different class
C: number of time series assigned to different cluster and belong to the same class
D: number of time series assigned to same cluster and belong to the different class