(testing signal)

Tag: clustering

K Means Clustering Project (Pieran Data)

For this project we will attempt to use KMeans Clustering to cluster Universities into to two groups, Private and Public. It is very important to note, we actually have the labels for this data set, but we will NOT use them for the KMeans clustering algorithm, since that is an unsupervised learning algorithm.

When using the Kmeans algorithm under normal circumstances, it is because you don’t have labels. In this case we will use the labels to try to get an idea of how well the algorithm performed, but you won’t usually do this for Kmeans, so the classification report and confusion matrix at the end of this project, don’t truly make sense in a real world setting!.

The Data
We will use a data frame with 777 observations on the following 18 variables.… Read more...

What Machine Learning Can Do for Security

Machine learning can be applied in various ways in security, for instance, in malware analysis, to make predictions, and for clustering security events. It can also be used to detect previously unknown attacks with no established signature.

Wendy Edwards, a software developer interested in the intersection of cybersecurity and data science, spoke about applying machine learning to security at The Diana Initiative 2021.

Artificial Intelligence (AI) can be applied to detect anomalies by finding unusual patterns. But unusual doesn’t necessarily mean malicious, as Edwards…

Scikit Learn 1.0: New Features in Python Machine Learning Library

Scikit-learn is the most popular open-source and free python machine learning library for Data scientists and Machine learning practitioners. The scikit-learn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction.Read the full story

Text Similarity using K-Shingling, Minhashing and LSH(Locality Sensitive Hashing)

Natural Language ProcessingText Similarity using K-Shingling, Minhashing, and LSH(Locality Sensitive Hashing)Text similarity plays an important role in Natural Language Processing (NLP) and there are several areas where this has been utilized extensively. Some of the applications include Information retrieval, text categorization, topic detection, machine translation, text summarization, document clustering, plagiarism detection, news recommendation, etc. encompassing almost all domains.But…

How to Improve Deep Learning Forecasts for Time Series

Clustering its benefits.Clustering time series data before fitting can improve accuracy by ~33% — src.Figure 1: time series clustering example. Image by author.In 2021, researchers at UCLA developed a method that can improve model fit on many different time series’. By aggregating similarly structured data and fitting a model to each group, our models can specialize.While fairly straightforward to implement, as with any other complex deep learning method, we are often…

A Step By Step Implementation of Principal Component Analysis

A step-by-step tutorial to explain the working of PCA and implementing it from scratch in pythonImage By AuthorIntroductionPrincipal Component Analysis or PCA is a commonly used dimensionality reduction method. It works by computing the principal components and performing a change of basis. It retains the data in the direction of maximum variance. The reduced features are uncorrelated with each other. These features can be used for unsupervised clustering and classification. To reduce…

SAP BW Data Mining Analytics: Clustering Reporting (Part 4, final)

Summary
Clustering analysis is another standard method available with SAP BW Data Mining. The clustering models based on this method may apply various combinations of parameters (e.g., maximum number of clusters, minimum fraction of inter-cluster hops per iteration, etc.) in order to implement various clustering approaches. The clustering-specific reporting of the method makes possible analysis of the modeling results. In this paper we would like to discuss extensions to the standard…

Equivalence class clustering and bottom-up lattice traversal (ECLAT)

This article has been excerpted from my book, Models and Algorithms for Unlabelled Data.

Next time you visit a nearby grocery store, look around inside the store and the arrangements of various items. You would find shelves with items like milk, eggs, bread, sugar, washing powder, soaps, fruits, vegetables, cookies and various other items neatly stacked. Have you ever wondered what is the logic of this arrangement and how these items are laid out? Why certain products are kept near to each…

Topic Modeling: Algorithms, Techniques, and Application

Used in unsupervised machine learning tasks, Topic Modeling is treated as a form of tagging and primarily used for information retrieval wherein it helps in query expansion. It is vastly used in mapping user preference in topics across search engineers. The main applications of Topic Modeling are classification, categorization, summarization of documents. AI methodologies associated with genetics, social media, and computer vision tasks are associated with Topic Modeling. It also powers analysis on social networks pertaining to the sentiments of users.

Topic Modeling Difference and Related Algorithms

Topic Modeling is performed on unsupervised information and has a clear distinction from text classification and clustering tasks.

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Machine Learning Model Selection strategy for Data Scientists and ML Engineers

“Thus learning is not possible without inductive bias, and now the question is how to c right bias. This is called model selection.” ETHEN ALPAYDIN (2004) p33 (Introduction to Machine Learning)

Really there are many more definitions concerning Model Selection. In this article, we are going to discuss Model Selection and its strategy for Data Scientists and Machine Learning Engineers.

An ML model(s) are always constructed using various mathematical frameworks and that would generate predictions based on the nature of the dataset and finding patterns out of it.

Most of them are really confused between two terminologies in machine learning – ML-Model and ML-Algorithm. Even me too. But over the period I got to understand the thin line between these two terms.… Read more...

How to train an Out-of-Memory Data with Scikit-learn

Essential guide to incremental learning using the partial_fit API

Image by PublicDomainPictures from Pixabay

Scikit-learn is a popular Python package among the data science community, as it offers the implementation of various classification, regression, and clustering algorithms. One can train a classification or regression machine learning model in few lines of Python code using the scikit-learn package.

Pandas is another popular Python library that offers to handle and preprocessing data prior to feeding it to a scikit-learn model. One can easily process and train an in-memory dataset (data that can fit into the RAM memory) using Pandas and Scikit-learn packages, but when it comes to working with a large dataset or out-of-memory dataset (data that cannot fit into the RAM memory), it fails, and cause memory issue.

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The Mystery of Feature Scaling is Finally Solved

Photo by Danist Soh on Unsplash
Dave Guggenheim

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20 Machine Learning Projects That Will Get You Hired

 

By Khushbu Shah, Content Manager at ProjectPro.

The AI and Machine Learning industry is booming like never before. As of 2021, the increase in AI usage across businesses will create $2.9 trillion of business value. AI has automated many industries across the globe and changed the way they operate. Most large companies incorporate AI to maximize productivity in their workflow, and industries like marketing and healthcare have undergone a paradigm shift due to the consolidation of AI.

Image Source : Unsplash

Due to this, there has been an increasing demand in the past few years for AI professionals. There has almost been a 100% increase in AI and machine learning-related job postings from 2015 to 2018. This number has grown since and is projected to rise in 2021.

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Plants evolved complexity in two bursts — with a 250-million-year hiatus

A Stanford-led study reveals that rather than evolving gradually over hundreds of millions of years, land plants underwent major diversification in two dramatic bursts, 250 million years apart. The first occurred early in plant history, giving rise to the development of seeds, and the second took place during the diversification of flowering plants.

The research uses a novel but simple metric to classify plant complexity based on the arrangement and number of basic parts in their reproductive structures. While scientists have long assumed that plants became more complex with the advent of seeds and flowers, the new findings, published Sept. 17 in Science, offer insight to the timing and magnitude of those changes.

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SAP BW Data Mining Analytics: Model Reporting (Part 1)

SAP BW Data Mining allows creating data mining models that implement respective analysis methods (either supplied by SAP as built-in with SAP BW Data Mining or supplied by certified vendors). Although analysis methods available via SAP BW Data Mining provide extensive reporting and visualizations, there could be a need for additional model- and method-related analytics that would facilitate management and deployment of the content created with SAP BW Data Mining. In this paper we will present the following analytics:

  • Dashboard – SAP BW Data Mining Model Reporting

Business Requirements

The main use of SAP BW Data Mining is creation of models based on analysis methods and of analysis processes based on the models.

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Mastering Clustering Methods in Python

Clustering is the process of separating different parts of data based on common characteristics. Disparate industries including retail, finance and healthcare use clustering techniques for various analytical tasks. In retail, clustering can help identify distinct consumer populations, which can then allow a company to create targeted advertising based on consumer demographics that may be too complicated to inspect manually. In finance, clustering can detect different forms of illegal market activity like orderbook spoofing in which traders deceitfully place large orders to pressure other traders into buying or selling an asset. In healthcare, clustering methods have been used to figure out patient cost patterns, early onset neurological disorders and cancer gene expression.

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K-means clustering: find my tribe!

What is K-means clustering?

Find hidden information

At its core K-means clustering is an algorithm that tries to categorize sample data according to certain rules. If our sample data are vectors of real numbers, the simplest criteria is the distance between the data.

Suppose I have 4 sample data points in the two dimensional space:

(0, 0), (0, 1), (0, 3), (0, 4)

Data points: (0, 0), (0, 1), (0, 3), (0, 4) (Image by Author)

and suppose I know that I have two groups, how do I find the center of the two groups and the member data points of each group?

First step is to pick two starting points for each of the groups. In this example, it’s quite easy to simply eyeball and, say, pick (0, 0) as starting point for the first group and (0, 3) as starting point for the second group.

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Image Segmentation with Classical Computer Vision-Based Approaches

Classical Computer Vision-Based Image Segmentation methods like Thresholding, Region-Based, Edge Detection Based and Morphological Segmentation are explained in 1 post to make a quick start with this domain

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Image Segmentation with Clustering

The Fundamentals of K-Means and Fuzzy-C Means Clustering and their usage for Image Segmentation

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Clustering Product Names with Python — Part 2

Using Natural Language Processing (NLP) and K-Means to cluster unlabelled text in Python

This guide goes through how we can use Natural Language Processing (NLP) and K-means in Python to automatically cluster unlabelled product names to quickly understand what kinds of products are in a data set.

This article is Part 2 and will cover: K-means Clustering, Assessing Cluster Quality and Finetuning.

If you haven’t already, please read Part 1 which covers: Preprocessing and Vectorisation.

Now that we have our word matrices, let’s get clustering.

This is the sexy part: clustering our word matrices.

K-means clustering allocates data points into discrete groups based on their similarity or proximity to each other.

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Clustering Product Names with Python — Part 1

The method consists of the following steps:

  • Preprocessing the text (the food names) into clean words so that we can turn it into numerical data.
  • Vectorisation which is the process of turning words into numerical features to prepare for machine learning.
  • Applying K-means clustering, an unsupervised machine learning algorithm, to group food names with similar words together.
  • Assessing cluster quality through cluster labelling and visualisation.
  • Finetuning steps 1–4 to improve cluster quality.

This article is Part 1 and will cover: Preprocessing and Vectorisation.

Be sure to also check out Part 2 which will cover: K-means Clustering, Assessing Cluster Quality and Finetuning.

Full disclosure: this data set actually comes with a column ‘Classification Name’ with 268 categories but for demonstration purposes, let’s pretend it’s not there 😉

This guide will use Pandas, NumPy, scikit-learn, FuzzyWuzzy, Matplotlib and Plotly.

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Clustering types with various applications

Clustering types and their usage areas are explained with python implementation

Ibrahim Kovan

Unlabeled datasets can be grouped by considering their similar properties with the unsupervised learning technique. However, the point of view of these similar features is different in each algorithm. Unsupervised learning provides detailed information about the dataset as well as labeling the data.

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Graph Neural Networks Combined with Semantic Reasoning Deliver ‘Total AI’

The ability for machines to reason—not just identify patterns in massive data amounts, but make rule or logic based inferences on domain specific knowledge—is foundational to Artificial Intelligence. The growing momentum around Neuro-Symbolic AI and the increasing reliance on Graph Analytics demonstrate how important these developments are for the enterprise.

Combining AI’s symbolic knowledge processing with its statistical branch (typified by machine learning) produces the best prescriptive outcomes, delivers total AI, and is swiftly becoming necessary to tackle enterprise scale applications of mission-critical processes like foretelling equipment failure, optimizing healthcare treatment, and maximizing customer relationships.

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