Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
Outlier analysis,Chapter-12, Data Mining: Concepts and TechniquesAshikur Rahman
This slide is prepared for a course of Dept. of CSE, Islamic Univresity of Technology (IUT).
Course: CSE 4739- Data Mining
This topic is based on:
Data Mining: Concepts and Techniques
Book by Jiawei Han
Chapter 12
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
Outlier analysis,Chapter-12, Data Mining: Concepts and TechniquesAshikur Rahman
This slide is prepared for a course of Dept. of CSE, Islamic Univresity of Technology (IUT).
Course: CSE 4739- Data Mining
This topic is based on:
Data Mining: Concepts and Techniques
Book by Jiawei Han
Chapter 12
Unsupervised Machine Learning Ml And How It WorksSlideTeam
Unsupervised Machine Learning ML and how it works is for the mid level managers to give information about what is unsupervised machine learning, types of unsupervised learning, and its disadvantages. You can also know how unsupervised machine learning works to understand supervised machine learning in a better way for business growth. https://bit.ly/3fTQ7iI
The Basics of Statistics for Data Science By StatisticiansStat Analytica
Want to learn data science, but don't know how to start learn data science from scratch? Here in this presentation you will going to learn the basics of statistics for data science. Start learn these basic statistics to get the good command over data science.
Missing data handling is typically done in an ad-hoc way. Without understanding the repurcussions of a missing data handling technique, approaches that only let you get to the "next step" in your analytics pipeline leads to terrible outputs, conclusions that aren't robust and biased estimates. Handling missing data in data sets requires a structured approach. In this workshop, we will cover the key tenets of handling missing data in a structured way
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
Principal Component Analysis (PCA) and LDA PPT SlidesAbhishekKumar4995
Machine learning (ML) technique use for Dimension reduction, feature extraction and analyzing huge amount of data are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are easily and interactively explained with scatter plot graph , 2D and 3D projection of Principal components(PCs) for better understanding.
Module 4: Model Selection and EvaluationSara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
A Mixture Model of Hubness and PCA for Detection of Projected OutliersZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm .This combination not only
provides the great boost to the detection of outliers but also enhances its accuracy and precision.
Unsupervised Machine Learning Ml And How It WorksSlideTeam
Unsupervised Machine Learning ML and how it works is for the mid level managers to give information about what is unsupervised machine learning, types of unsupervised learning, and its disadvantages. You can also know how unsupervised machine learning works to understand supervised machine learning in a better way for business growth. https://bit.ly/3fTQ7iI
The Basics of Statistics for Data Science By StatisticiansStat Analytica
Want to learn data science, but don't know how to start learn data science from scratch? Here in this presentation you will going to learn the basics of statistics for data science. Start learn these basic statistics to get the good command over data science.
Missing data handling is typically done in an ad-hoc way. Without understanding the repurcussions of a missing data handling technique, approaches that only let you get to the "next step" in your analytics pipeline leads to terrible outputs, conclusions that aren't robust and biased estimates. Handling missing data in data sets requires a structured approach. In this workshop, we will cover the key tenets of handling missing data in a structured way
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
Principal Component Analysis (PCA) and LDA PPT SlidesAbhishekKumar4995
Machine learning (ML) technique use for Dimension reduction, feature extraction and analyzing huge amount of data are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are easily and interactively explained with scatter plot graph , 2D and 3D projection of Principal components(PCs) for better understanding.
Module 4: Model Selection and EvaluationSara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
A Mixture Model of Hubness and PCA for Detection of Projected OutliersZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm .This combination not only
provides the great boost to the detection of outliers but also enhances its accuracy and precision.
A MIXTURE MODEL OF HUBNESS AND PCA FOR DETECTION OF PROJECTED OUTLIERSZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm
A Mixture Model of Hubness and PCA for Detection of Projected OutliersZac Darcy
With the Advancement of time and technology, Outlier Mining methodologies help to sift through the large
amount of interesting data patterns and winnows the malicious data entering in any field of concern. It has
become indispensible to build not only a robust and a generalised model for anomaly detection but also to
dress the same model with extra features like utmost accuracy and precision. Although the K-means
algorithm is one of the most popular, unsupervised, unique and the easiest clustering algorithm, yet it can
be used to dovetail PCA with hubness and the robust model formed from Guassian Mixture to build a very
generalised and a robust anomaly detection system. A major loophole of the K-means algorithm is its
constant attempt to find the local minima and result in a cluster that leads to ambiguity. In this paper, an
attempt has done to combine K-means algorithm with PCA technique that results in the formation of more
closely centred clusters that work more accurately with K-means algorithm .
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Anomaly Detection using multidimensional reduction Principal Component AnalysisIOSR Journals
Anomaly detection has been an important research topic in data mining and machine learning. Many
real-world applications such as intrusion or credit card fraud detection require an effective and efficient
framework to identify deviated data instances. However, most anomaly detection methods are typically
implemented in batch mode, and thus cannot be easily extended to large-scale problems without sacrificing
computation and memory requirements. In this paper, we propose multidimensional reduction principal
component analysis (MdrPCA) algorithm to address this problem, and we aim at detecting the presence of
outliers from a large amount of data via an online updating technique. Unlike prior principal component
analysis (PCA)-based approaches, we do not store the entire data matrix or covariance matrix, and thus our
approach is especially of interest in online or large-scale problems. By using multidimensional reduction PCA
the target instance and extracting the principal direction of the data, the proposed MdrPCA allows us to
determine the anomaly of the target instance according to the variation of the resulting dominant eigenvector.
Since our MdrPCA need not perform eigen analysis explicitly, the proposed framework is favored for online
applications which have computation or memory limitations. Compared with the well-known power method for
PCA and other popular anomaly detection algorithms
Multiple Linear Regression Models in Outlier Detection IJORCS
Identifying anomalous values in the real-world database is important both for improving the quality of original data and for reducing the impact of anomalous values in the process of knowledge discovery in databases. Such anomalous values give useful information to the data analyst in discovering useful patterns. Through isolation, these data may be separated and analyzed. The analysis of outliers and influential points is an important step of the regression diagnostics. In this paper, our aim is to detect the points which are very different from the others points. They do not seem to belong to a particular population and behave differently. If these influential points are to be removed it will lead to a different model. Distinction between these points is not always obvious and clear. Hence several indicators are used for identifying and analyzing outliers. Existing methods of outlier detection are based on manual inspection of graphically represented data. In this paper, we present a new approach in automating the process of detecting and isolating outliers. Impact of anomalous values on the dataset has been established by using two indicators DFFITS and Cook’sD. The process is based on modeling the human perception of exceptional values by using multiple linear regression analysis.
However, the success or failure of a project relies on proper data cleaning. Professional data scientists usually invest a very large portion of their time in this step because of the belief that “Better data beats fancier algorithms”.
This report contains:-
1. what is data analytics, its usages, its types.
2. Tools used for data analytics
3. description of Classification
4. description of the association
5. description of clustering
6. decision tree, SVM modelling etc with example
Acorn Recovery: Restore IT infra within minutesIP ServerOne
Introducing Acorn Recovery as a Service, a simple, fast, and secure managed disaster recovery (DRaaS) by IP ServerOne. A DR solution that helps restore your IT infra within minutes.
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Orkestra
UIIN Conference, Madrid, 27-29 May 2024
James Wilson, Orkestra and Deusto Business School
Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
Have you ever wondered how search works while visiting an e-commerce site, internal website, or searching through other types of online resources? Look no further than this informative session on the ways that taxonomies help end-users navigate the internet! Hear from taxonomists and other information professionals who have first-hand experience creating and working with taxonomies that aid in navigation, search, and discovery across a range of disciplines.
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
5. Before we
begin..
Slides?
You can download them at
https://bit.ly/introtoadvml-week1-slides
Questions?
Post your questions in the QA box, one of
the panelists will answer!
Issues?
Chat directly with the panelists if you are
facing any issues!
6. If you torture the data enough, it will
confess to anything.
Ronald Coase
7. Agenda
1. What are Outliers?
2. Type of Outliers
3. Causes of outliers
4. Impact of Outliers on Data
5. Detecting and Fixing Outliers
6. Other Ways of Handling Outliers
7. References & Assignment
8. Implementation on Google Colab
9. What are Outliers?
Outliers are extreme values that
fall a long way outside of the other
observations. For example, in a
gaussian distribution, outliers may
be values on the tails of the
distribution.
https://towardsdatascience.com/a-brief-overview-of-outlier-detection-techniques-1e0b2c19e561
Fig 1 Gaussian Distribution showing position
of outliers
10. What are Outliers?
We will generally define outliers
as samples that are exceptionally
far from the mainstream of the
data.
-Page 33, Applied Predictive
Modeling, 2013.
https://towardsdatascience.com/a-brief-overview-of-outlier-detection-techniques-1e0b2c19e561, https://research.vu.nl/ws/portalfiles/portal/21334642/hoofdstuk+3.pdf
Fig 2a Data points and
Outliers
Fig 2b Data points and
Regression Model
12. Genesis of Outliers
Most common causes of outliers on a data set
✓ Errors in Data entry (human errors)
✓ Errors during Measurement (instrument errors)
✓ Experimental errors (data extraction or experiment planning/executing errors)
✓ dummy outliers created to test detection methods
✓ Data processing errors (data manipulation or data set unintended mutations)
✓ Sampling errors (extracting or mixing data from wrong or various sources)
✓ Natural (not an error, novelties in data)
Those causes that are not a product of an error are called novelties.
14. Types of Outliers
1. Univariate Outliers
2. Multivariate Outliers
3. Point or Global Outliers
4. Collective Outliers
5. Contextual Outliers
6. Other Outliers e.g. Outliers in Time Series Data:
a. Additive Outliers
b. Innovative Outliers
18. A subset of data points within a data set is
considered anomalous if those values as a
collection deviate significantly from the
entire data set, but the values of the
individual data
points are not
themselves
anomalous in
either a
contextual or
global sense.
Univariate
Multivariate
Point/Global
Collective
Contextual/Conditional
Other
→ Additive
→ Innovative
https://www.researchgate.net/figure/Collective-outlier-in-an-human-ECG-output-corresponding-to-an-Atrial-Premature_fig3_267964435
Outlier Types
Fig 7 CollectiveOutlier
19. Outlier Types
Univariate
Multivariate
Point/Global
Collective
Contextual/Conditiona
l
Other
→ Additive
→ Innovative
A data point is considered a contextual outlier if its
value significantly deviates from the rest the data
points in the same context.
https://www.semanticscholar.org/paper/Contextual-Outlier-Detection-in-Sensor-Data-Using-Haque-Mineno/5cc5b6760d2de45add2959b150044f4c70a78aea/figure/4
Fig 8 Contextual or Conditional Outlier
22. Play Ground
Which of the following is NOT a type of outlier?
1. Multivariate outlier
2. Global outlier
3. Angular outlier
4. Contextual outlier
NB: Drop your choice of answer in the chat section
23. Outliers as a Tsunami in Data
What is the Impact of Outliers on a dataset?
● Outliers is able to rapidly change the results of the data analysis and statistical
modeling. There are several negative impacts of outliers in the data set:
● It increases the error variance and reduces the power of statistical tests.
● If the outliers are non-randomly distributed, they can decrease normality.
● They can bias or influence
estimates that may be of
substantive interest.
● They can also impact the
basic assumption of
Regression, and other
statistical model assumptions.
https://journals.sagepub.com/doi/pdf/10.1177/1475921717748878
Fig 11 Calculations showing the impact of outliers
24. Outliers as a Tsunami in Data
What is the Impact of Outliers on a dataset?
https://www.kdnuggets.com/2018/08/make-machine-learning-models-robust-outliers.html
Fig 12 Impact of
outliers on Model
Performance
25. Play Ground
Some data scientist argued that, outliers are can be used in a
positive way for improving model performance while others
disagree.
What is your take on the argument?
1. Agree
2. Disagree
NB: Drop your choosing answer in the chat section
26. Tea Break
Exercise: You have some datasets and you are building
a model to classify the dataset. You observe that, your
classification model has three ways to accomplish this.
Choose between
1,2 and 3,
the line of best
fit that can best
classify your
data?
NB: Take note of the
outlier in the dataset.
Drop your selected
number on the chat
section.
https://classroom.udacity.com/courses/ud120
Fig 13 Choosing the right model
28. Detecting
Outliers in
Data
There are several ways of detecting outliers in a
dataset. Meanwhile we will discuss two most
frequently used and effective methods.
Most commonly used method to detect outliers is
visualization. We use various visualization
methods, like Box-plot, Histogram, Scatter Plot
(above, we have used box plot and scatter plot for
visualization). Whatever the visualization maybe, it
all depends on some ML algorithms;
→ Multivariate detection
→ Dimensionality Reduction
29. Detecting Outliers in Data
→ Multivariate detection can be identified with the use of Mahalanobis distance, which is
the distance of a data point from the calculated centroid of the other cases where the
centroid is calculated as the intersection of the mean of the variables being assessed.
↳ Mahalanobis Distance
↳ Cooke’s Distance
→ Dimensionality Reduction
(PCA, LDA)
https://journals.sagepub.com/doi/pdf/10.1177/1475921717748878
Fig 14 Detecting outliers using Mahalanobis Distance
30. Detecting & Fixing Outliers in Data
https://www.researchgate.net/figure/Outliers-by-Cooks-distance-with-a-red-line-plotted-to-indicate-division-to-outlierhood_fig1_323265273
Fig 15
Detecting
outliers using
Cooke’s
Distance
→ Multivariate detection can be identified with the use of Mahalanobis distance, which is
the distance of a data point from the calculated centroid of the other cases where the
centroid is calculated as the intersection of the mean of the variables being assessed.
↳ Mahalanobis Distance
↳ Cooke’s Distance
A general rule of
thumb is that
observations with a
Cook’s D of more than
3 times the mean, μ,
is a possible outlier.
→ Dimensionality Reduction
(PCA, LDA)
31. Detecting & Fixing Outliers in Data
→ Multivariate detection (Mahalanobis Distance, Cooke’s Distance)
→ Dimensionality Reduction is a technique for minimizing dimensionalities of data in other
to reduce over-fitting and avoid making a model complex. When dimensionality is
reduced by projecting all points onto a line, the outlier is mapped into the center of the
reduced data set.
↳ Principal Component
Analysis (PCA)
↳ Linear Discriminant
Analysis (LDA)
32. Detecting & Fixing Outliers in Data
https://sebastianraschka.com/faq/docs/lda-vs-pca.html
→ Multivariate detection (Mahalanobis Distance, Cooke’s Distance)
→ Dimensionality Reduction is a technique for minimizing dimensionalities of data in other
to reduce over-fitting and avoid making a model complex. When dimensionality is
reduced by projecting all points onto a line, the outlier is mapped into the center of the
reduced data set.
↳ Principal Component Analysis (PCA)
PCA is a technique that finds the directions
of maximal variance. PCA aims to find the
directions of maximum variance in high-
dimensional data and projects it onto a new
subspace with equal or fewer dimensions
than the original data.
↳ Linear Discriminant Analysis (LDA)
Fig 16 The
Process of
a PCA
33. Detecting & Fixing Outliers in Data
https://www.researchgate.net/figure/Principal-component-analysis-example-PC-1-contains-the-most-energy-of-the-data-but-does_fig2_279177589
→ Multivariate detection (Mahalanobis Distance, Cooke’s Distance)
→ Dimensionality Reduction is a technique for minimizing dimensionalities of data in other
to reduce over-fitting and avoid making a model complex. When dimensionality is
reduced by projecting all points onto a line, the outlier is mapped into the center of the
reduced data set.
↳ Principal Component Analysis (PCA)
PCA is a technique that finds the directions
of maximal variance. PCA aims to find the
directions of maximum variance in high-
dimensional data and projects it onto a new
subspace with equal or fewer dimensions
than the original data.
↳ Linear Discriminant Analysis (LDA) Fig 17 An example of PCA for DR
34. Detecting & Fixing Outliers in Data
→ Multivariate detection (Mahalanobis Distance, Cooke’s Distance)
→ Dimensionality Reduction is a technique for minimizing dimensionalities of data in other
to reduce over-fitting and avoid making a model complex. When dimensionality is
reduced by projecting all points onto a line, the outlier is mapped into the center of the
reduced data set.
↳ Principal Component Analysis (PCA)
↳ Linear Discriminant Analysis (LDA)
LDA attempts to find a feature
subspace that maximizes
class separability.
Fig 18
The
process
of an LDA
35. Differences between PDA & LDA
Principal Component Analysis
(PCA)
Linear Discriminant Analysis
(LDA)
Supervised Learning Unsupervised Learning
Not effective with Labelled
Class of Dataset
Works best with Large
Labelled Dataset
Used for feature classification Used for data classification
38. Recap
→ What are Outliers?
→ Type of Outliers
↳ Univariate Outliers, Multivariate Outliers, Point or
Global Outliers, Collective Outliers, Contextual
Outliers
→ Causes of outliers
↳ Errors: Human, Natural, Sampling, Data
processing, etc
→ Impact of Outliers on Data
↳ error variance, decrease normality, bias or
influence estimates, reduce model performance
→ Detecting and Fixing Outliers
↳ Multivariate detection, Dimensionality Reduction
→ Other Ways of Handling Outliers
↳ Missing Values, High Correlations (Spearman’s
Correlation), Low Variance
39. Organize
Your
Research
Useful Links
↳ A Brief Overview of Outlier Detection
Techniques (TowardsDataScience)
↳ PCA on Iris Dataset (Github)
↳ LDA on Iris Dataset (Github)
Assignment
↳ Principal Component Analysis
(TowardsDataScience)
41. Homework
1. Letter Recognition Dataset
(Multi-dimensional)
2. New York Times Corpus (Time-
series for event detection)
3. Yahoo Labs: Server Traffic
(Multi-variate time-series)