Clustering


Unsupervised Learning Algorithms in One Picture - DataScienceCentral.com

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Unsupervised learning algorithms are "unsupervised" because you let them run without direct supervision. You feed the data into the algorithm, and the algorithm figures out the patterns. The following picture shows the differences between three of the most popular unsupervised learning algorithms: Principal Component Analysis, k-Means clustering and Hierarchical clustering. The three are closely related, because data clustering is a type of data reduction; PCA can be viewed as a continuous counterpart of K-Means (see Ding & He, 2004).


5 Clustering Methods in Machine Learning

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In the beginning, let's have some common terminologies overview, A cluster is a group of objects that lie under the same class, or in other words, objects with similar properties are grouped in one cluster, and dissimilar objects are collected in another cluster. And, clustering is the process of classifying objects into a number of groups wherein each group, objects are very similar to each other than those objects in other groups. Simply, segmenting groups with similar properties/behaviour and assign them into clusters. Being an important analysis method in machine learning, clustering is used for identifying patterns and structure in labelled and unlabelled datasets. Clustering is exploratory data analysis techniques that can identify subgroups in data such that data points in each same subgroup (cluster) are very similar to each other and data points in separate clusters have different characteristics.


Day 30: 60 days of Data Science and Machine Learning Series

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This article explains what data engineers are and what their varied tasks and duties are. Seaborn is a very prominent library used during Exploratory Data Analysis of any data science project you are working upon. At times, this cohort could feel overwhelming due to the sheer volume of material I would need to learn and practice.


Hierarchical Clustering: Explain It To Me Like I'm 10

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This is part numero tres of the Explaining Machine Learning Algorithms to a 10-Year Old series. If you read the two previous ones about XGBoost Regression and K-Means Clustering, then you know the drill. We have a scary-sounding algorithm, so let's strip it of its scary bits and understand the simple intuition behind it. In the same vein as K-Means Clustering, today we are going to talk about another popular clustering algorithm -- Hierarchical Clustering. Let's say a clothing store has collected the ages of 9 of its customers, labeled C1-C9, and the amount each of them spent at the store in the last month.


What is K-Means?

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K-Means Clustering is an unsupervised learning algorithm that is used to solve the clustering problems in machine learning or data science. In this article, I would be giving you a detailed explanation and how this model works. Unsupervised Learning method K-Means Clustering divides the unlabeled dataset into various clusters. K specifies the number of pre-defined clusters that must be produced throughout the process; for example, if K 2, two clusters will be created, and if K 3, three clusters will be created, and so on. It allows us to cluster data into distinct groups and provides a simple technique to determine the categories of groups in an unlabeled dataset without any training. It's a centroid-based approach, which means that each cluster has its own centroid.


Understanding K-Means Clustering Algorithm - Analytics Vidhya

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With the rising use of the Internet in today's society, the quantity of data created is incomprehensibly huge. Even though the nature of individual data is straightforward, the sheer amount of data to be analyzed makes processing difficult for even computers. To manage such procedures, we need large data analysis tools. Data mining methods and techniques, in conjunction with machine learning, enable us to analyze large amounts of data in an intelligible manner. It is capable of classifying unlabeled data into a predetermined number of clusters based on similarities (k).


A Practical Introduction to Hierarchical clustering from scikit-learn

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Hierarchical clustering is part of the group of unsupervised learning models known as clustering. This means that we don't have a defined target variable unlike in traditional regression or classification tasks. The point of this machine learning algorithm, therefore, is to identify distinct clusters of objects that share similar characteristics by using defined distance metrics on the selected variables. Other machine learning algorithms that fit within this family include Kmeans or DBscan. This specific algorithm comes in two main flavours or forms: top-down or bottom-up.


Visually Grounded Models of Spoken Language: A Survey of Datasets, Architectures and Evaluation Techniques

Journal of Artificial Intelligence Research

This survey provides an overview of the evolution of visually grounded models of spoken language over the last 20 years. Such models are inspired by the observation that when children pick up a language, they rely on a wide range of indirect and noisy clues, crucially including signals from the visual modality co-occurring with spoken utterances. Several fields have made important contributions to this approach to modeling or mimicking the process of learning language: Machine Learning, Natural Language and Speech Processing, Computer Vision and Cognitive Science. The current paper brings together these contributions in order to provide a useful introduction and overview for practitioners in all these areas. We discuss the central research questions addressed, the timeline of developments, and the datasets which enabled much of this work. We then summarize the main modeling architectures and offer an exhaustive overview of the evaluation metrics and analysis techniques.


RVN Algorithm

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When we need to cluster a data set, the first couple of algorithms we might look into are K means, DB scan, or hierarchical clustering algorithm. Those classic clustering algorithms always treat each data point as a dot. However, those data points usually have size or boundary(bounding box) in real life. Ignoring the edge of points might cause further bias. RVN algorithm is a method that considers points and the bounding box of each point.


Towards Continuous Consistency Axiom

arXiv.org Artificial Intelligence

Development of new algorithms in the area of machine learning, especially clustering, comparative studies of such algorithms as well as testing according to software engineering principles requires availability of labeled data sets. While standard benchmarks are made available, a broader range of such data sets is necessary in order to avoid the problem of overfitting. In this context, theoretical works on axiomatization of clustering algorithms, especially axioms on clustering preserving transformations are quite a cheap way to produce labeled data sets from existing ones. However, the frequently cited axiomatic system of Kleinberg:2002, as we show in this paper, is not applicable for finite dimensional Euclidean spaces, in which many algorithms like $k$-means, operate. In particular, the so-called outer-consistency axiom fails upon making small changes in datapoint positions and inner-consistency axiom is valid only for identity transformation in general settings. Hence we propose an alternative axiomatic system, in which Kleinberg's inner consistency axiom is replaced by a centric consistency axiom and outer consistency axiom is replaced by motion consistency axiom. We demonstrate that the new system is satisfiable for a hierarchical version of $k$-means with auto-adjusted $k$, hence it is not contradictory. Additionally, as $k$-means creates convex clusters only, we demonstrate that it is possible to create a version detecting concave clusters and still the axiomatic system can be satisfied. The practical application area of such an axiomatic system may be the generation of new labeled test data from existent ones for clustering algorithm testing. %We propose the gravitational consistency as a replacement which does not have this deficiency.