The document discusses various algorithms for big data clustering. It begins by covering preprocessing techniques such as data reduction. It then covers hierarchical, prototype-based, density-based, grid-based, and scalability clustering algorithms. Specific algorithms discussed include K-means, K-medoids, PAM, CLARA/CLARANS, DBSCAN, OPTICS, MR-DBSCAN, DBCURE, and hierarchical algorithms like PINK and l-SL. The document emphasizes techniques for scaling these algorithms to large datasets, including partitioning, sampling, approximation strategies, and MapReduce implementations.
This is very simple introduction to Clustering with some real world example. At the end of lecture I use stackOverflow API to test some clustering. I also wants to try facebook but it has some problem with it's API
This is very simple introduction to Clustering with some real world example. At the end of lecture I use stackOverflow API to test some clustering. I also wants to try facebook but it has some problem with it's API
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 Clustering Algorithm | K Means Clustering Example | Machine Learning ...Simplilearn
This K-Means clustering algorithm presentation will take you through the machine learning introduction, types of clustering algorithms, k-means clustering, how does K-Means clustering work and at least explains K-Means clustering by taking a real life use case. This Machine Learning algorithm tutorial video is ideal for beginners to learn how K-Means clustering work.
Below topics are covered in this K-Means Clustering Algorithm presentation:
1. Types of Machine Learning?
2. What is K-Means Clustering?
3. Applications of K-Means Clustering
4. Common distance measure
5. How does K-Means Clustering work?
6. K-Means Clustering Algorithm
7. Demo: k-Means Clustering
8. Use case: Color compression
- - - - - - - -
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.
- - - - - - -
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.
- - - - - -
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
- - - - - - -
It is a data mining technique used to place the data elements into their related groups. Clustering is the process of partitioning the data (or objects) into the same class, The data in one class is more similar to each other than to those in other cluster.
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
An Efficient Clustering Method for Aggregation on Data FragmentsIJMER
Clustering is an important step in the process of data analysis with applications to numerous fields. Clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a quality cluster. Existing clustering aggregation algorithms are applied directly to large number of data points. The algorithms are inefficient if the number of data points is large. This project defines an efficient approach for clustering aggregation based on data fragments. In fragment-based approach, a data fragment is any subset of the data. To increase the efficiency of the proposed approach, the clustering aggregation can be performed directly on data fragments under comparison measure and normalized mutual information measures for clustering aggregation, enhanced clustering aggregation algorithms are described. To show the minimal computational complexity. (Agglomerative, Furthest, and Local Search); nevertheless, which increases the accuracy.
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 Clustering Algorithm | K Means Clustering Example | Machine Learning ...Simplilearn
This K-Means clustering algorithm presentation will take you through the machine learning introduction, types of clustering algorithms, k-means clustering, how does K-Means clustering work and at least explains K-Means clustering by taking a real life use case. This Machine Learning algorithm tutorial video is ideal for beginners to learn how K-Means clustering work.
Below topics are covered in this K-Means Clustering Algorithm presentation:
1. Types of Machine Learning?
2. What is K-Means Clustering?
3. Applications of K-Means Clustering
4. Common distance measure
5. How does K-Means Clustering work?
6. K-Means Clustering Algorithm
7. Demo: k-Means Clustering
8. Use case: Color compression
- - - - - - - -
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.
- - - - - - -
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.
- - - - - -
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
- - - - - - -
It is a data mining technique used to place the data elements into their related groups. Clustering is the process of partitioning the data (or objects) into the same class, The data in one class is more similar to each other than to those in other cluster.
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
An Efficient Clustering Method for Aggregation on Data FragmentsIJMER
Clustering is an important step in the process of data analysis with applications to numerous fields. Clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a quality cluster. Existing clustering aggregation algorithms are applied directly to large number of data points. The algorithms are inefficient if the number of data points is large. This project defines an efficient approach for clustering aggregation based on data fragments. In fragment-based approach, a data fragment is any subset of the data. To increase the efficiency of the proposed approach, the clustering aggregation can be performed directly on data fragments under comparison measure and normalized mutual information measures for clustering aggregation, enhanced clustering aggregation algorithms are described. To show the minimal computational complexity. (Agglomerative, Furthest, and Local Search); nevertheless, which increases the accuracy.
Scalability has been an essential factor for any kind of computational algorithm while considering its performance. In this Big Data era, gathering of large amounts of data is becoming easy. Data analysis on Big Data is not feasible using the existing Machine Learning (ML) algorithms and it perceives them to perform poorly. This is due to the fact that the computational logic for these algorithms is previously designed in sequential way. MapReduce becomes the solution for handling billions of data efficiently. In this report we discuss the basic building block for the computations behind ML algorithms, two different attempts to parallelize machine learning algorithms using MapReduce and a brief description on the overhead in parallelization of ML algorithms.
Experimental study of Data clustering using k- Means and modified algorithmsIJDKP
The k- Means clustering algorithm is an old algorithm that has been intensely researched owing to its ease
and simplicity of implementation. Clustering algorithm has a broad attraction and usefulness in
exploratory data analysis. This paper presents results of the experimental study of different approaches to
k- Means clustering, thereby comparing results on different datasets using Original k-Means and other
modified algorithms implemented using MATLAB R2009b. The results are calculated on some performance
measures such as no. of iterations, no. of points misclassified, accuracy, Silhouette validity index and
execution time
TOWARDS REDUCTION OF DATA FLOW IN A DISTRIBUTED NETWORK USING PRINCIPAL COMPO...cscpconf
For performing distributed data mining two approaches are possible: First, data from several sources are copied to a data warehouse and mining algorithms are applied in it. Secondly,
mining can performed at the local sites and the results can be aggregated. When the number of
features is high, a lot of bandwidth is consumed in transferring datasets to a centralized location. For this dimensionality reduction can be done at the local sites. In dimensionality reduction a certain encoding is applied on data so as to obtain its compressed form. The
reduced features thus obtained at the local sites are aggregated and data mining algorithms are applied on them. There are several methods of performing dimensionality reduction. Two most important ones are Discrete Wavelet Transforms (DWT) and Principal Component Analysis (PCA). Here a detailed study is done on how PCA could be useful in reducing data flow across a distributed network.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
Clustering of high dimensionality data which can be seen in almost all fields these days is becoming
very tedious process. The key disadvantage of high dimensional data which we can pen down is curse
of dimensionality. As the magnitude of datasets grows the data points become sparse and density of
area becomes less making it difficult to cluster that data which further reduces the performance of
traditional algorithms used for clustering. Semi-supervised clustering algorithms aim to improve
clustering results using limited supervision. The supervision is generally given as pair wise
constraints; such constraints are natural for graphs, yet most semi-supervised clustering algorithms are
designed for data represented as vectors [2]. In this paper, we unify vector-based and graph-based
approaches. We first show that a recently-proposed objective function for semi-supervised clustering
based on Hidden Markov Random Fields, with squared Euclidean distance and a certain class of
constraint penalty functions, can be expressed as a special case of the global kernel k-means objective
[3]. A recent theoretical connection between global kernel k-means and several graph clustering
objectives enables us to perform semi-supervised clustering of data. In particular, some methods have
been proposed for semi supervised clustering based on pair wise similarity or dissimilarity
information. In this paper, we propose a kernel approach for semi supervised clustering and present in
detail two special cases of this kernel approach.
DECISION TREE CLUSTERING: A COLUMNSTORES TUPLE RECONSTRUCTIONcscpconf
Column-Stores has gained market share due to promising physical storage alternative for analytical queries. However, for multi-attribute queries column-stores pays performance
penalties due to on-the-fly tuple reconstruction. This paper presents an adaptive approach for reducing tuple reconstruction time. Proposed approach exploits decision tree algorithm to
cluster attributes for each projection and also eliminates frequent database scanning.Experimentations with TPC-H data shows the effectiveness of proposed approach.
Extended pso algorithm for improvement problems k means clustering algorithmIJMIT JOURNAL
The clustering is a without monitoring process and one of the most common data mining techniques. The
purpose of clustering is grouping similar data together in a group, so were most similar to each other in a
cluster and the difference with most other instances in the cluster are. In this paper we focus on clustering
partition k-means, due to ease of implementation and high-speed performance of large data sets, After 30
year it is still very popular among the developed clustering algorithm and then for improvement problem of
placing of k-means algorithm in local optimal, we pose extended PSO algorithm, that its name is ECPSO.
Our new algorithm is able to be cause of exit from local optimal and with high percent produce the
problem’s optimal answer. The probe of results show that mooted algorithm have better performance
regards as other clustering algorithms specially in two index, the carefulness of clustering and the quality
of clustering.
Tank Battle - A simple game powered by JMonkey engineFarzad Nozarian
A simple two player java-based game powered by JMonkey game engine which implements many standard object oriented design patterns such as Singleton, Composite, Strategy and etc.
A tutorial presentation based on github.com/amplab/shark documentation.
I gave this presentation at Amirkabir University of Technology as Teaching Assistant of Cloud Computing course of Dr. Amir H. Payberah in spring semester 2015.
A tutorial presentation based on hbase.apache.org documentation.
I gave this presentation at Amirkabir University of Technology as Teaching Assistant of Cloud Computing course of Dr. Amir H. Payberah in spring semester 2015.
A tutorial presentation based on hadoop.apache.org documentation.
I gave this presentation at Amirkabir University of Technology as Teaching Assistant of Cloud Computing course of Dr. Amir H. Payberah in spring semester 2015.
A tutorial presentation based on hadoop.apache.org documentation.
I gave this presentation at Amirkabir University of Technology as Teaching Assistant of Cloud Computing course of Dr. Amir H. Payberah in spring semester 2015.
A tutorial presentation based on spark.apache.org documentation.
I gave this presentation at Amirkabir University of Technology as Teaching Assistant of Cloud Computing course of Dr. Amir H. Payberah in spring semester 2015.
A tutorial presentation based on storm.apache.org documentation.
I gave this presentation at Amirkabir University of Technology as Teaching Assistant of Cloud Computing course of Dr. Amir H. Payberah in spring semester 2015.
Big Data Processing in Cloud Computing EnvironmentsFarzad Nozarian
This is my Seminar presentation, adopted from a paper with the same name (Big Data Processing in Cloud Computing Environments), and it is about various issues of Big Data, from its definitions and applications to processing it in cloud computing environments. It also addresses the Big Data technologies and focuses on MapReduce and Hadoop.
A presentation about Yahoo! S4 and Apache S4. I gave this presentation for Cloud Computing course of Dr. Payberah @ AUT fall 2014.
The lecturer's references are Yahoo! S4 paper and Apache S4 website.
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
2. Preprocessing
Goals:
1. To assure the quality of the data by reducing the noisy and irrelevant information that it
could contain
2. To reduce the size of the dataset, so the computational cost of the discovery task is also
reduced.
Reducing the size of dataset:
◦ Number of instances
◦ addressed by sampling (the sampled dataset should holds the same information that the whole dataset)
◦ Dimensionality reduction
◦ Feature selection
◦ Feature extraction
2
3. Clustering algorithms
Hierarchical methods
◦ Divisive
◦ Agglomerative
Based on similarity matrix for each pair of examples
Some algorithm consider this matrix as Graph;
Other algorithm reduce the matrix each iteration by merging two groups.
The main drawback of these algorithms is their computational cost. (o(n2))
Scanning the dataset many times!
3
4. Prototype/model based clustering
Prototype and model based clustering assume that clusters fit to a specific shape.
Goal: Discover how different numbers of these shapes can explain the spatial distribution of
the data.
Must used prototype based clustering is K-Means.
◦ K-Means assumes that clusters are defined by their center (the prototype) and have spherical shapes.
◦ To feet this shape K-Means minimizing the distances from the examples to these centers.
◦ solved iteratively using a gradient descent algorithm.
4
5. Density based clustering
DBSCAN
OPTICS is an extension of the original DBSCAN that uses heuristics to find good values for
DBSCAN parameters.
The main drawback of this methods comes from the cost of finding the nearest neighbors for
an example.
Indexing is a solution, but may be degraded with the number of dimensions to a linear search.
5
6. Grid based clustering
The basic idea: divide the space of instances in hyperrectangular cells by discretizing the
attributes of the dataset.
Clusters of arbitrary shapes.
Each cell is summarized by the sufficient statistics of the examples it contains.
Usually scale well, but it depends on the granularity of the discretization of the space of
examples.
The strategies used to prune the search space allow to largely reduce the computational cost
6
8. One-pass strategies
Reduce the number of scans of the data to only one.
This constraint may be usually forced by the circumstance that the dataset can not fit in
memory and it has to be obtained from disk.
This is used to perform a preprocess of the dataset.
This results in two stages algorithms, a first one that applies the one-pass strategy and a
second one that process in memory a summary of the data obtained by the first stage.
8
9. Summarization Strategies
Purpose: obtain a coarse approximation of the data without losing the information that
represent the different densities of examples.
Sufficient statistics like mean and variance.
The summarization can be performed single level, as a preprocess that is feed to a cluster
algorithm.
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10. Sampling/batch strategies
Purpose: Allow to perform the processing in main memory for a part of the dataset.
In case of more than one sample of the data: The algorithm should be able to process raw data
and cluster summaries.
They scale on the size of the sampling and not on the size of the whole dataset.
The use of batches assume that the data can be processed sequentially and that after applying
a clustering algorithm to a batch, the result can be merged with the results from previous
batches.
Data stream!
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11. Approximation strategies
These strategies assume that some computations can be saved or approximated with reduced
or null impact on the final result.
Algorithm dependent.
Most costly part of clustering algorithms corresponds to distance computation among
instances or among instances and prototypes.
E.g, some of these algorithms are iterative and the decision about what partition is assigned to
an example does not change after a few iterations. If this can be determined at an early stage, all
these distance computations can be avoided in successive iterations.
This strategy is usually combined with a summarization strategy where groups of examples are
reduced to a point that is used to decide if the decision can be performed using only that point
or the distances to all the examples have to be computed.
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12. Divide and conquer strategies
Data can be divided in multiple independent datasets and that the clustering results can be
then merged on a final model.
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14. PINK: A Scalable Algorithm for Single-Linkage Hierarchical Clustering on
Distributed-Memory Architectures (2013) (northwestern)
A scalable parallel algorithm for single-linkage hierarchical clustering based on decomposing a
problem instance into two different types of subproblems.
As PINK does not explicitly store a distance matrix, it can be applied to much larger problem
sizes.
Algorithm:
◦ Divide a large hierarchical clustering problem instance into a set of smaller sub-problems
◦ Calculate the hierarchical clustering dendrogram for each of these sub-problems
◦ Reconstruct the solution for the original dataset by combining the solutions to the sub-problems.
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15. Leader-single-link (l-SL): A distance based clustering
method for arbitrary shaped clusters in large datasets (2011)
Divides the clustering process in two steps:
◦ One pass clustering algorithm: resulting in a set of cluster summaries that reduce the size of the
dataset.
◦ This new dataset fits in memory and can be processed using a single link hierarchical clustering
algorithm.
Leaders clustering method: is a single data-scan distance based partitional clustering method.
For a given threshold distance τ, it produces a set of leaders L incrementally. For each pattern
𝑥, if there is a leader 𝑙 ϵ L such that 𝑥 − 𝑙 ≤ 𝜏, then 𝑥 is assigned to a cluster represented by 𝑙.
If there is no such leader, then 𝑥 becomes a new leader.
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One-passSummarization
16. Leader-single-link (l-SL) (cont.)
The k-means also is a leader algorithm but it is applicable to numerical dataset only and scans
dataset more than once before convergence.
After producing the leaders, the leaders set is further clustered using SL method with cut-off
distance ℎ which results in clustering of leaders.
Finally, each leader is replaced by its followers to produce final clustering.
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18. PDBSCAN
1. Divide the input into several partitions, and distribute these partitions to the available
computers
2. Cluster partitions concurrently using DBSCAN
3. Combine or merge the clustering's of the partitions into a clustering of the whole database.
4. In distributed environment we should care about data placement:
◦ Load balancing: the partitions should be almost of equal size if we assume that all computers have the
same performance
◦ Minimized communication cost: should avoid accessing those data located on any of the other
computers
◦ Distributed data access: This is not applicable for MR!
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DivideandconquerdR*-tree
19. PDBSCAN (Cont.)
Algorithm is based on the R*-tree, provides not only a spatial data placement strategy for
clustering, but also efficient access to spatial data in a shared nothing architecture through the
replication of indices.
Proposed data placement solution: grouping the MBRs of leaf nodes of the R*-tree into N
partitions such that the nearby MBRs should be assigned to the same partition and the
partitions should be almost of equal size with respect to the number of MBRs.
How this solution can be achieved? use space filling Hilbert curves
For a given R*-tree, this method works as follows:
◦ Every data page of the R*-tree is assigned to a Hilbert value according to its center of gravity. So,
successive data pages will be close in space.
◦ Sort the list of pairs by ascending Hilbert values.
◦ If the R*-tree has d data pages and we have n slaves, every slave obtains d/n data pages of the sorted
list
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20. PDBSCAN (Cont.)
Proposed efficient access to the distributed data solution: replicate the directory of the R*-tree
on all available computers (dR*-tree)
Now the PDBSCAN algorithm:
◦ Starts with an arbitrary point p within S and retrieves all points which are density-reachable from p
◦ If p is not a core point, no points are density-reachable from p: visits the next point in partition S
◦ If all members of C are contained in S: C is also a cluster
◦ If there are members of C outside of S: C may need to be merged with another cluster found call C a
merging candidate
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21. PDBSCAN (Cont.)
The master PDBSCAN receives a list of merging candidates from every SLAVE.
PDBSCAN collects all the lists L it receives and assigns them to a list LL.
A merging function is noting else a nested loop that check for each pair of cluster if their
intersection aren’t empty!
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22. MR-DBSCAN (2011)
Implement it by a 4-stages MapReduce paradigm.
Contributions: quick partitioning strategy for large scale non-indexed data.
Challenges of designing DBSCAN in MapReduce:
◦ Data interchange mechanism is limited. Data transferring between map and reduce is not encouraged.
◦ MapReduce doesn’t provide any mechanism such as R-tree, KD-tree to improve multidimensional search.
◦ Maximum parallelism can be achieved when the data is well balanced.
PDBSCAN was has been the basis of their work.
However it aggregate intermediate results in a single node, and MR-DBSCAN optimize this
issue.
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GridMR
23. MR-DBSCAN (2011) (Cont.)
Stage 1: Preprocessing:
◦ Main challenges for a partitioning strategy are:
◦ Load balancing
◦ Minimize communication or shuffling cost (all related records, including the data within space Si and
its halo replication from bordering spaces, should easily map to a same key and be shuffled to target
reducer)
◦ What is the problem of spatial index? (disadvantages of indexing in MapReduce)
◦ Most of them are required to do iteration recursion to get a hierarchical structure that is not practical
in MapReduce. (BUT WHAT ABOUT SPARK?!)
◦ For large scale data its hierarchical index could reach one tenth of its original data size, which in huge
and hard to handle.
Proposed solution: grid file (divide the data domain in dimension i into mi portions, each of
which is considered as a mini bucket.)
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24. MR-DBSCAN (2011) (Cont.)
Stage 2: Local DBSCAN :
◦ In PDBSCAN each thread could access not just its partition data but global data
during the processing of local DBSCAN algorithm. !!BAD in MapReduce!!
The local DBSCAN algorithm will only scan data and extend core points
within space Si.
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When the cluster scan extends outside Si, assumed that a record q outside Si is directly-density-
reachable from a core point p in Si, we will not detect whether q is a core point anymore.
q will be marked as ‘On-queue’ status and put into Merge Candidates set (MC set) with core point p
as well.
25. MR-DBSCAN (2011) (Cont.)
Stage 3: Find Merging Mapping:
They optimized single node aggregation bottleneck in this section!
In PDBSCAN to merge the cluster from different subspaces:
◦ Collect the entire MC into a big list LL
◦ Among all the points in the list, execute a nested loop to find out whether two item with a
same point id are from different clusters.
◦ If found, merging the cluster.
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26. MR-DBSCAN (2011) (Cont.)
Stage 4: Merge
Stage 4.1: Build Global Mapping:
We get several id lists of clusters to be merged for each two bordering space. (i, c1)<->(i+1, c2)
The output of this section is the mapping ((gridID, localclusterID), globalclusterID) for each local
cluster in each partition.
Stage 4.2: Merge and Relabel:
The final stage of algorithm is streaming all the local clustered records over the map-reduce
process and replacing their local cluster id with a new global cluster id (gid) based on the
mapping profile from Stage 4.1.
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27. DBCURE (2014)
DBCURE utilizes ellipsoidal τ-neighborhoods instead of spherical ε-neighborhoods and has a
desirable property of being less sensitive to density parameters.
DBCURE is more suitable than OPTICS for being parallelized with MapReduce since the
ellipsoidal τ-neighborhood of each point can be determined in parallel.
User R*-tree efficiently to find the ellipsoidal τ-neighborhoods of a given point.
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R*-treeIndexingMRGrid
29. K-Means Algorithms
Its popularity can be attributed to several reasons:
1. It is conceptually simple and easy to implement.
2. It is versatile, i.e., almost every aspect of the algorithm (initialization, distance function,
termination criterion, etc.) can be modified. (This is evidenced by hundreds of
publications over the last fifty years that extend k-means in a variety of ways.)
3. It has a time complexity that is linear in N, D, and K (in general, D ≪ N and K ≪ N)
4. It has a storage complexity that is linear in N, D, and K
5. It is guaranteed to converge at a quadratic rate
6. It is invariant to data ordering, i.e., random shuffling of the data points (MapReduce
balance!)
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30. K-Means Algorithms (Cont.)
k-means has several significant disadvantages:
1. It requires the number of clusters, K, to be specified in advance.
◦ Can be determined automatically by means of various internal/relative cluster validity
measures.
2. It can only detect compact, hyper spherical clusters that are well separated.
◦ Can be alleviated by using a more general distance function such as the Mahalanobis distance,
which permits the detection of hyper ellipsoidal clusters.
3. It is sensitive to noise and outlier points.
◦ Can be addressed by outlier pruning or by using a more robust distance function such as the
city-block (ℓ1) distance.
4. It often converges to a local minimum of the criterion function.
◦ For the same reason, it is highly sensitive to the selection of the initial centers
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31. K-Means Algorithms (Cont.)
The Obstacles of Very Large Datasets Clustering Using K-Means:
◦ computational complexity of distance calculations;
◦ The number of iterations which significantly increases when the number of sample data increases.
Proposed idea to solve these obstacles:
◦ Solved by using MapReduce model to distribute computations
◦ Solved by using two-stages K-Means algorithm or K-Means++ algorithm
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32. K-Medoids
Both K-Means and K-Medoids attempt to minimize the distance between points labeled to be
in a cluster and a point designated as the center of that cluster.
K-Medoids chooses data points as centers (medoids or exemplars) and works with an arbitrary
matrix of distances between data points instead of 𝜄2
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33. PAM: Partitioning Around Medoids
1. Initialize: randomly select k of the n data points as the medoids
2. Associate each data point to the closest medoid. ("closest" here is defined using any
valid distance metric, most commonly Euclidean distance, Manhattan distance or Minkowski distance)
3. For each medoid m
For each non-medoid data point o
Swap m and o and compute the total cost of the configuration
4. Select the configuration with the lowest cost.
5. Repeat steps 2 to 4 until there is no change in the medoid
33
34. CLARA/CLARANS
Reduces the number of medoids' calculations through sampling.
A small portion of data is firstly selected from the whole datasets and then PAM is used to
search the cluster medoids
34
Sampling
35. Fast clustering using MapReduce (2011, KDD)
K-center: the goal is to choose the centers such that the maximum distance between a center and a point
assigned to it is minimized.
K-median: It is a variation of k-means clustering where instead of calculating the mean for each cluster
to determine its centroid, one instead calculates the median. (the 1-norm distance metric, as opposed to the
square of the 2-norm)
Assume that the input is a weighted complete graph G = (V;E) that has an edge xy between any two
points in V , and the weight of the edge xy is d(x; y)
First idea: Adoption of existing algorithms to MR:
◦ Partition input across machines
◦ Each machine perform computation to sparsify data
◦ Results are collected in single machine and perform computation and final solution.
Unfortunately the total running time of the algorithm can be quite large:
It runs costly clustering algorithm on Ω(𝑘 𝑛)
35
ParallelSamplingMR
36. Fast clustering using MapReduce (2011, KDD) (Cont.)
This algorithm uses Iterative-Sample as a subroutine:
◦ Performs the following computation in parallel across the machines:
◦ In each round, it adds a small sample of points to the final sample, it determines which points are “well
represented” by the sample, and it recursively considersonly the points that are not well represented
After a good/strong sampling, they put the sampled points on a single machine and run a clustering
algorithm on just the sampled points.
They also describe about 3 page about their mathematical proof of their good iterative sampling
algorithm.
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37. PK-Means: Parallel K-Means Clustering Based on MapReduce
(2099)
Map function: Assign each sample to the closest center
Reduce function: Performs the procedure of updating the new centers.
Combiner function: Deal with partial combination of the intermediate values with the same
key within the same map task
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MR
41. FMR.K-Means: Fast K-Means Clustering for Very Large Datasets Based on
MapReduce Combined with a New Cutting Method (2015)
Presents a new approach for reducing the number of iterations of K-Means algorithm
Based on Parallel K-Means based on the MapReduce.
Propose a new method called cutting off the last iterations based on differences between
centers of each cluster of two adjacent iterations.
41
MRIterationElimination
42. Canopy Clustering (KDD 2000)
Canopy works with datasets that either:
◦ Having millions of data points
◦ Thousands of dimensions
◦ Thousands of clusters
Key idea: Using a cheap, approximate distance measure to efficiently divide the data into
overlapping subsets (Canopies), then clustering is performed by measuring exact distances only
between points that occur in a common canopy.
Use domain-specific features in order to design a cheap distance metric and efficiently create
canopies using the metric.
A fast distance metrics for text used by search engines are based on the inverted index.
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ApproximationTwo-stage
43. Fuzzy C-Means (FCM)
Given a finite set of data, the algorithm returns a list of c cluster centers and a partition matrix,
where each element of matrix tells the degree to which element xi belongs to cluster ci.
Like the k-means algorithm, the FCM aims to minimize an objective function:
This differs from the k-means objective function by the addition of the membership values wij
and the fuzzifier m.
The fuzzifier m determines the level of cluster fuzziness.
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44. K-Means + Canopy: An Integrated Clustering Framework
Using Optimized K-means with Firefly and Canopies (2015)
Proposed by integration of two meta-heuristic algorithms: Firefly algorithm and Canopy
44
ApproximationTwo-stage
45. K-medoids Clustering Based on MapReduce and
Optimal Search of Medoids (2014)
Proposed an improved algorithm based on MapReduce and optimal search of medoids.
According to the basic properties of triangular geometry, this paper reduced calculation of
distances among data elements to help search medoids quickly and reduce the calculation
complexity of k-medoids.
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MROptimalSearch