Clustering of data is an increasingly important task for many data scientists. This talk will explore the challenge of hierarchical clustering of text data for summarisation purposes. We'll take a look at some great solutions now available to Python users including the relevant Scikit Learn libraries, via Elasticsearch (with the carrot2 plugin), and check out visualisations from both approaches.
https://www.youtube.com/watch?v=KFs9pBAetOo
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
Quick introduction to community detection.
Structural properties of real world networks, definition of "communities", fundamental techniques and evaluation measures.
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
Quick introduction to community detection.
Structural properties of real world networks, definition of "communities", fundamental techniques and evaluation measures.
DBScan stands for Density-Based Spatial Clustering of Applications with Noise.
DBScan Concepts
DBScan Parameters
DBScan Connectivity and Reachability
DBScan Algorithm , Flowchart and Example
Advantages and Disadvantages of DBScan
DBScan Complexity
Outliers related question and its solution.
Network centrality measures and their effectivenessemapesce
Often centrality measures are used in social network analysis. The goal of this presentation is to explain how different centrality works and how they can be compared.
Centrality measures covered: degree, closeness, harmonic, Lin's index, betweenness, eigenvector, seeley's index, pagerank, hits, SALSA
Machine Learning and Data Mining: 08 Clustering: Hierarchical Pier Luca Lanzi
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. This lecture introduces hierarchical clustering.
DBScan stands for Density-Based Spatial Clustering of Applications with Noise.
DBScan Concepts
DBScan Parameters
DBScan Connectivity and Reachability
DBScan Algorithm , Flowchart and Example
Advantages and Disadvantages of DBScan
DBScan Complexity
Outliers related question and its solution.
Network centrality measures and their effectivenessemapesce
Often centrality measures are used in social network analysis. The goal of this presentation is to explain how different centrality works and how they can be compared.
Centrality measures covered: degree, closeness, harmonic, Lin's index, betweenness, eigenvector, seeley's index, pagerank, hits, SALSA
Machine Learning and Data Mining: 08 Clustering: Hierarchical Pier Luca Lanzi
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. This lecture introduces hierarchical clustering.
A sprint thru Python's Natural Language ToolKit, presented at SFPython on 9/14/2011. Covers tokenization, part of speech tagging, chunking & NER, text classification, and training text classifiers with nltk-trainer.
Machine Learning and Data Mining: 05 Advanced Association Rule MiningPier Luca Lanzi
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. This lecture overviews the FP-growth algorithm, methods for multilevel rules, correlation rules, and sequential rules.
Social networks are not new, even though websites like Facebook and Twitter might make you want to believe they are; and trust me- I’m not talking about Myspace! Social networks are extremely interesting models for human behavior, whose study dates back to the early twentieth century. However, because of those websites, data scientists have access to much more data than the anthropologists who studied the networks of tribes!
Because networks take a relationship-centered view of the world, the data structures that we will analyze model real world behaviors and community. Through a suite of algorithms derived from mathematical Graph theory we are able to compute and predict behavior of individuals and communities through these types of analyses. Clearly this has a number of practical applications from recommendation to law enforcement to election prediction, and more.
Create Linked Open Data (LOD) Microthesauri using Art & Architecture Thesaurus (AAT) LOD. View and manage options by a non-techy person. Everyone can use, create,
derive from, & map to AAT microthesauri and make the digital collection become LOD-ready dataset.
Deduplication and Author-Disambiguation of Streaming Records via Supervised M...Spark Summit
Here we present a general supervised framework for record deduplication and author-disambiguation via Spark. This work differentiates itself by – Application of Databricks and AWS makes this a scalable implementation. Compute resources are comparably lower than traditional legacy technology using big boxes 24/7. Scalability is crucial as Elsevier’s Scopus data, the biggest scientific abstract repository, covers roughly 250 million authorships from 70 million abstracts covering a few hundred years. – We create a fingerprint for each content by deep learning and/or word2vec algorithms to expedite pairwise similarity calculation. These encoders substantially reduce compute time while maintaining semantic similarity (unlike traditional TFIDF or predefined taxonomies). We will briefly discuss how to optimize word2vec training with high parallelization. Moreover, we show how these encoders can be used to derive a standard representation for all our entities namely such as documents, authors, users, journals, etc. This standard representation can simplify the recommendation problem into a pairwise similarity search and hence it can offer a basic recommender for cross-product applications where we may not have a dedicate recommender engine designed. – Traditional author-disambiguation or record deduplication algorithms are batch-processing with small to no training data. However, we have roughly 25 million authorships that are manually curated or corrected upon user feedback. Hence, it is crucial to maintain historical profiles and hence we have developed a machine learning implementation to deal with data streams and process them in mini batches or one document at a time. We will discuss how to measure the accuracy of such a system, how to tune it and how to process the raw data of pairwise similarity function into final clusters. Lessons learned from this talk can help all sort of companies where they want to integrate their data or deduplicate their user/customer/product databases.
Keynote presentation delivered at ELAG 2013 in Gent, Belgium, on May 29 2013. Discusses Research Objects and the relationship to work my team has been involved in during the past couple of years: OAI-ORE, Open Annotation, Memento.
Keynote: SemSci 2017: Enabling Open Semantic Science
1st International Workshop co-located with ISWC 2017, October 2017, Vienna, Austria,
https://semsci.github.io/semSci2017/
Abstract
We have all grown up with the research article and article collections (let’s call them libraries) as the prime means of scientific discourse. But research output is more than just the rhetorical narrative. The experimental methods, computational codes, data, algorithms, workflows, Standard Operating Procedures, samples and so on are the objects of research that enable reuse and reproduction of scientific experiments, and they too need to be examined and exchanged as research knowledge.
We can think of “Research Objects” as different types and as packages all the components of an investigation. If we stop thinking of publishing papers and start thinking of releasing Research Objects (software), then scholar exchange is a new game: ROs and their content evolve; they are multi-authored and their authorship evolves; they are a mix of virtual and embedded, and so on.
But first, some baby steps before we get carried away with a new vision of scholarly communication. Many journals (e.g. eLife, F1000, Elsevier) are just figuring out how to package together the supplementary materials of a paper. Data catalogues are figuring out how to virtually package multiple datasets scattered across many repositories to keep the integrated experimental context.
Research Objects [1] (http://researchobject.org/) is a framework by which the many, nested and contributed components of research can be packaged together in a systematic way, and their context, provenance and relationships richly described. The brave new world of containerisation provides the containers and Linked Data provides the metadata framework for the container manifest construction and profiles. It’s not just theory, but also in practice with examples in Systems Biology modelling, Bioinformatics computational workflows, and Health Informatics data exchange. I’ll talk about why and how we got here, the framework and examples, and what we need to do.
[1] Sean Bechhofer, Iain Buchan, David De Roure, Paolo Missier, John Ainsworth, Jiten Bhagat, Philip Couch, Don Cruickshank, Mark Delderfield, Ian Dunlop, Matthew Gamble, Danius Michaelides, Stuart Owen, David Newman, Shoaib Sufi, Carole Goble, Why linked data is not enough for scientists, In Future Generation Computer Systems, Volume 29, Issue 2, 2013, Pages 599-611, ISSN 0167-739X, https://doi.org/10.1016/j.future.2011.08.004
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
Collaborations in the Extreme: The rise of open code development in the scie...Kelle Cruz
Video: https://www.simonsfoundation.org/event/collaborations-in-the-extreme-the-rise-of-open-code-development-in-the-scientific-community/
The internet is changing the scientific landscape by fostering international, interdisciplinary and collaborative software development. More than ever before, software is a crucial component of any scientific result. The ability to easily share code is reshaping expectations about reproducibility -- a fundamental tenet of the scientific process. In this lecture, Kelle Cruz will briefly provide the backstory of how these shifts have come about, describe some of the most impactful open source projects, and discuss efforts currently underway aimed at ensuring these community-led projects are sustainable and receive support.
Research Objects for improved sharing and reproducibilityOscar Corcho
Presentation about the usage of Research Objects to improve scientific experiment sharing and reproducibility, given at the Dagstuhl Perspective Workshop on the intersection between Computer Sciences and Psychology (July 2015)
The World Wide Web is moving from a Web of hyper-linked documents to a Web of linked data. Thanks to the Semantic Web technological stack and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets connected with each other to form the so called LOD cloud. As of today, we have tons of RDF data available in the Web of Data, but only a few applications really exploit their potential power. The availability of such data is for sure an opportunity to feed personalized information access tools such as recommender systems. We will show how to plug Linked Open Data in a recommendation engine in order to build a new generation of LOD-enabled applications.
(Lecture given @ the 11th Reasoning Web Summer School - Berlin - August 1, 2015)
Recommending Semantic Nearest Neighbors Using Storm and DatoAshok Venkatesan
In this talk, we present how SU has used Dato Graphlab-Create along with Apache Storm to build a minimum viable online pipeline for computing item similarity over item attributes – a key component in contextual recommendations.
Keynote given at the workshop for Artificial Intelligence meets the Web of Data on Pragmatic Semantics.
In this keynote I argue that the Web of Data is a Complex System or Marketplace of Ideas rather than a classical Database, and that the model theory on which classical semantics are based is not appropriate in all situations, and propose an alternative "Pragmatic Semantics" based on optimisation of possible interpretations. .
Similar to Hierarchical clustering in Python and beyond (20)
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Business update Q1 2024 Lar España Real Estate SOCIMI
Hierarchical clustering in Python and beyond
1. Hierarchical clustering
in Python & elsewhere
For @PyDataConf London, June 2015, by Frank Kelly
Data Scientist, Engineer @analyticsseo
@norhustla
2. Hierarchical
Clustering
Theory Practice Visualisation
Origins & definitions
Methods & considerations
Hierachical theory
Metrics & performance
My use case
Python libraries
Example
Static
Interactive
Further ideas
All opinions expressed are my own
5. Clustering is an unsupervised learning
problem
"SLINK-Gaussian-data" by Chire - Own work. Licensed under CC BY-SA 3.0 via Wikimedia Commons -
https://commons.wikimedia.org/wiki/File:SLINK-Gaussian-data.svg#/media/File:SLINK-Gaussian-data.svg
based on some
notion of similarity.
whereby we aim to
group subsets of
entities with one
another
8. Two
main
purposes
Exploratory analysis – standalone tool
(Data mining)
As a component of a supervised learning
pipeline (in which distinct classifiers or
regression models are trained for each
cluster).
(Machine Learning)
10. Use case: search keywords
RD
P
P
P
KW
KW
KW
KW
KW
CP
CP
KW
KW
KW
The
competition!
KW
KW
CP
CD
You
Opportunity!
CD = Competing domains
CP = Competitor’s pages
RD = Ranking domain
P = Your page
KW = Keyword
12. Use case: search keywords
KW…so we have found 100,000 new ‘s – now what?
How do we summarise and present these to a client?
13. Clients’ questions…
• Do search categories in general
align with my website structure?
• Which categories of opportunity
keywords have the highest
search volume, bring the most
visitors, revenue etc.?
• Which keywords are not
relevant?
15. Requirements
• Need: visual insights;
structure
• Allow targeting of
problem in hand
• May develop into a
semi- supervised
solution
16. • High-dimensional and sparse
data set
• Values correspond to word
frequencies
• Recommended methods
include: hierarchical
clustering, Kmeans with an
appropriate distance measure,
topic modelling (LDA, LSI),
co-clustering
Options for text
clustering?
19. Agglomerative
Start with many
“singleton” clusters
…
Merge 2 at a time
continuously
…
Build a hierarchy
Divisive
Start with a huge “macro”
cluster
…
Iteratively split into 2
groups
…
Build a hierarchy
20. Agglomerative method:
Linkage types
• Single (similarity between
most similar – based on nearest
neighbour - two elements)
• Complete (similarity between
most dissimilar two elements)
Attribution: https://www.coursera.org/course/clusteranalysis
21.
22.
23. Agglomerative method:
Linkage types
Average link
( avg. of similarity between
all inter-cluster pairs )
Computationally expensive (Na*Nb)
Trick: Centroid link (similarity
between centroid of two clusters)
Attribution: https://www.coursera.org/course/clusteranalysis
24. Ward’s criterion
• Minimise a function: total in-cluster variance
• As defined by, e.g.:
• Once merged, then the SSE will increase
(cluster becomes bigger) by:
https://en.wikipedia.org/wiki/Ward's_method
25. Divisive clustering
• Top-down approach
• Criterion to split: Ward’s criterion
• Handling noise: Use a threshold to determine
the termination criteria
Attribution: https://www.coursera.org/course/clusteranalysis
26. Similarity measures
This will certainly influence the shape of the
clusters!
• Numerical: Use a variation of the Manhattan
distance (e.g. City block, Euclidean)
• Binary: Manhattan, Jaccard co-efficient,
Hamming
• Text: Cosine similarity.
27. Cosine similarity
Represent a document by a bag of terms
Record the frequency of a particular term (word/ topic/ phrase)
If d1 and d2 are two term vectors,
…can thus calculate the similarity between them
Attribution: https://www.coursera.org/course/clusteranalysis
31. Text clustering:
preparations
• Add features where possible
o I added URL words to my word set
• Stem words
o Choose the right stemmer – too severe can be bad
• Stop words
o NLTK tokeniser
o Scikit learn TF-IDF tokeniser
• Low frequency cut-off
o 2 => words appearing less than twice in whole corpus
• High frequency cut-off
o 0.5 => words that appear in more than 50% of documents
• N-grams
o Single words, bi-grams, tri-grams
• Beware of foreign languages
o Separate datasets if possible
41. Life on the inside:
Elasticsearch
• Why not perform pre-processing and clustering
inside elasticsearch?
• Document store
• TF-IDF and other
• Stop words
• Language specific analysers
42. Elasticsearch
- try it ! -
• https://www.elastic.co/
• NoSQL document store
• Aggregations and stats
• Fast, distributed
• Quick to set up
45. Elasticsearch with
clustering – Utopia?
Carrot2’s Lingo3G in action :
http://search.carrot2.org/stable/search
Foamtree visualisation example
Visualisation of hierarchical structure possible for
large datasets via “lazy loading”
http://get.carrotsearch.com/foamtree/demo/demos/large.html
46. Limitations of hierarchical
clustering
• Can’t undo what’s done (divisive method, work
on sub clusters, cannot re-merge). Even true for
agglomerative (once merged will never split it
again)
• Every split or merge must be refined
• Methods may not scale well, checking all possible
pairs, complexity goes high
There are extensions: BIRCH, CURE and
CHAMELEON
48. Extra slide: Why work
inside the database?
1. Sharing data (management of)
Support concurrent access by multiple readers and writers
2. Data Model Enforcement
Make sure all applications see clean, organised data
3. Scale
Work with datasets too large to fit in memory (over a certain size,
need specialised algorithms to deal with the data -> bottleneck)
The database organises and exposes algorithms for you
conveniently
4. Flexibility
Use the data in new, unanticipated ways -> anticipate a broad set
of ways of accessing the data