This document introduces key concepts in data structures and algorithms. It defines an algorithm as a set of instructions to accomplish a task, and discusses how algorithms are evaluated based on their time and space complexity. It then defines data structures as organized data with relationships and permitted operations, and lists common examples like arrays, stacks and queues. The document also introduces abstract data types as mathematical models with defined operations.
The document discusses machine learning techniques for graphs and graph-parallel computing. It describes how graphs can model real-world data with entities as vertices and relationships as edges. Common machine learning tasks on graphs include identifying influential entities, finding communities, modeling dependencies, and predicting user behavior. The document introduces the concept of graph-parallel programming models that allow algorithms to be expressed by having each vertex perform computations based on its local neighborhood. It presents examples of graph algorithms like PageRank, product recommendations, and identifying leaders that can be implemented in a graph-parallel manner. Finally, it discusses challenges of analyzing large real-world graphs and how systems like GraphLab address these challenges through techniques like vertex-cuts and asynchronous execution.
GraphLab is a framework for parallel machine learning that represents data as a graph and uses shared tables. It allows users to define update, fold, and merge functions to modify vertex/edge states and aggregate data in shared tables. The GraphLab toolkit includes applications for topic modeling, graph analytics, clustering, collaborative filtering, and computer vision. Users can run GraphLab on Amazon EC2 by satisfying dependencies, compiling, and running examples like stochastic gradient descent for collaborative filtering on Netflix data.
This document discusses algorithms and their complexity. It provides an example of a linear search algorithm to find a target value in an array. The complexity of this algorithm is analyzed for the worst and average cases. In the worst case, the target is the last element and all n elements must be checked, resulting in O(n) time complexity. On average, about half the elements (n+1)/2 need to be checked, resulting in average time complexity of O(n).
The document provides an overview of data structures and algorithms. It discusses key topics like:
1) Different types of data structures including primitive, linear, non-linear, and arrays.
2) The importance of algorithms and how to write them using steps, comments, variables, and control structures.
3) Common operations on data structures like insertion, deletion, searching, and sorting.
4) Different looping and selection statements that can be used in algorithms like for, while, and if-then-else.
5) How arrays can be used as a structure to store multiple values in a contiguous block of memory.
K-Means clustering is an algorithm that partitions data points into k clusters based on their distances from initial cluster center points. It is commonly used for classification applications on large datasets and can be parallelized by duplicating cluster centers and processing each data point independently. Mahout provides implementations of K-Means clustering and other algorithms that can operate on distributed datasets stored in Hadoop SequenceFiles.
This article got published in the Software Developer's Journal's February Edition.
It describes the use of MapReduce paradigm to design Clustering algorithms and explain three algorithms using MapReduce.
- K-Means Clustering
- Canopy Clustering
- MinHash Clustering
A brief introduction to clustering with Scikit learn. In this presentation, we provide an overview with real examples of how to make use and optimize within k-means clustering.
This document introduces key concepts in data structures and algorithms. It defines an algorithm as a set of instructions to accomplish a task, and discusses how algorithms are evaluated based on their time and space complexity. It then defines data structures as organized data with relationships and permitted operations, and lists common examples like arrays, stacks and queues. The document also introduces abstract data types as mathematical models with defined operations.
The document discusses machine learning techniques for graphs and graph-parallel computing. It describes how graphs can model real-world data with entities as vertices and relationships as edges. Common machine learning tasks on graphs include identifying influential entities, finding communities, modeling dependencies, and predicting user behavior. The document introduces the concept of graph-parallel programming models that allow algorithms to be expressed by having each vertex perform computations based on its local neighborhood. It presents examples of graph algorithms like PageRank, product recommendations, and identifying leaders that can be implemented in a graph-parallel manner. Finally, it discusses challenges of analyzing large real-world graphs and how systems like GraphLab address these challenges through techniques like vertex-cuts and asynchronous execution.
GraphLab is a framework for parallel machine learning that represents data as a graph and uses shared tables. It allows users to define update, fold, and merge functions to modify vertex/edge states and aggregate data in shared tables. The GraphLab toolkit includes applications for topic modeling, graph analytics, clustering, collaborative filtering, and computer vision. Users can run GraphLab on Amazon EC2 by satisfying dependencies, compiling, and running examples like stochastic gradient descent for collaborative filtering on Netflix data.
This document discusses algorithms and their complexity. It provides an example of a linear search algorithm to find a target value in an array. The complexity of this algorithm is analyzed for the worst and average cases. In the worst case, the target is the last element and all n elements must be checked, resulting in O(n) time complexity. On average, about half the elements (n+1)/2 need to be checked, resulting in average time complexity of O(n).
The document provides an overview of data structures and algorithms. It discusses key topics like:
1) Different types of data structures including primitive, linear, non-linear, and arrays.
2) The importance of algorithms and how to write them using steps, comments, variables, and control structures.
3) Common operations on data structures like insertion, deletion, searching, and sorting.
4) Different looping and selection statements that can be used in algorithms like for, while, and if-then-else.
5) How arrays can be used as a structure to store multiple values in a contiguous block of memory.
K-Means clustering is an algorithm that partitions data points into k clusters based on their distances from initial cluster center points. It is commonly used for classification applications on large datasets and can be parallelized by duplicating cluster centers and processing each data point independently. Mahout provides implementations of K-Means clustering and other algorithms that can operate on distributed datasets stored in Hadoop SequenceFiles.
This article got published in the Software Developer's Journal's February Edition.
It describes the use of MapReduce paradigm to design Clustering algorithms and explain three algorithms using MapReduce.
- K-Means Clustering
- Canopy Clustering
- MinHash Clustering
A brief introduction to clustering with Scikit learn. In this presentation, we provide an overview with real examples of how to make use and optimize within k-means clustering.
A Fast and Dirty Intro to NetworkX (and D3)Lynn Cherny
Using the python lib NetworkX to calculate stats on a Twitter network, and then display the results in several D3.js visualizations. Links to demos and source files. I'm @arnicas and live at www.ghostweather.com.
This document provides a step-by-step guide to learning R. It begins with the basics of R, including downloading and installing R and R Studio, understanding the R environment and basic operations. It then covers R packages, vectors, data frames, scripts, and functions. The second section discusses data handling in R, including importing data from external files like CSV and SAS files, working with datasets, creating new variables, data manipulations, sorting, removing duplicates, and exporting data. The document is intended to guide users through the essential skills needed to work with data in R.
In this tutorial, we explore the most basic data structure in R, the vector. We cover everything from creating vectors to subsetting them in different ways.
This document provides an overview of data structures and algorithms. It introduces common linear data structures like stacks, queues, and linked lists. It discusses the need for abstract data types and different data types. It also covers implementing stacks as a linked list and common stack operations. Key applications of stacks include function call stacks which use a LIFO structure to remember the order of function calls and returns.
This document provides an outline of topics for learning the R programming language, including R basics, vectors and factors, arrays, matrices, lists, data frames, if/else statements, for loops, user defined functions, objects and classes, reading data files, string operations, and regular expressions. Key concepts covered are defining vectors and factors, performing operations on vectors, summarizing data, accessing and manipulating arrays and matrices, the structure and operations of data frames, using if/else statements and for/while loops, defining user functions, detecting object classes and converting between types, reading different file types into R, and using string and regular expression functions.
This document discusses methods in Java. It explains that a method declaration specifies the code that is executed when the method is called. Methods can take parameters as input and can return a value. The document provides an example of a Rectangle class with methods to calculate the area and perimeter of a rectangle. It includes the class definition with these methods and shows how to create Rectangle objects, call the methods on them, and print the results.
Entity Resolution is the task of disambiguating manifestations of real world entities through linking and grouping and is often an essential part of the data wrangling process. There are three primary tasks involved in entity resolution: deduplication, record linkage, and canonicalization; each of which serve to improve data quality by reducing irrelevant or repeated data, joining information from disparate records, and providing a single source of information to perform analytics upon. However, due to data quality issues (misspellings or incorrect data), schema variations in different sources, or simply different representations, entity resolution is not a straightforward process and most ER techniques utilize machine learning and other stochastic approaches.
Machine learning is the hacker art of describing the features of instances that we want to make predictions about, then fitting the data that describes those instances to a model form. Applied machine learning has come a long way from it's beginnings in academia, and with tools like Scikit-Learn, it's easier than ever to generate operational models for a wide variety of applications. Thanks to the ease and variety of the tools in Scikit-Learn, the primary job of the data scientist is model selection. Model selection involves performing feature engineering, hyperparameter tuning, and algorithm selection. These dimensions of machine learning often lead computer scientists towards automatic model selection via optimization (maximization) of a model's evaluation metric. However, the search space is large, and grid search approaches to machine learning can easily lead to failure and frustration. Human intuition is still essential to machine learning, and visual analysis in concert with automatic methods can allow data scientists to steer model selection towards better fitted models, faster. In this talk, we will discuss interactive visual methods for better understanding, steering, and tuning machine learning models.
This document discusses methods and constructors in Java. It defines methods as tasks that can take parameters and return values. Constructors are used to initialize new objects and are called when a new object is created using the new keyword. The document provides an example Rectangle class with methods to calculate area and perimeter, and a constructor to initialize length and width variables. It also shows calling these methods on Rectangle objects after creating them with the constructor.
1. Data structures organize data in memory for efficient access and processing. They represent relationships between data values through placement and linking of the values.
2. Algorithms are finite sets of instructions that take inputs, produce outputs, and terminate after a finite number of unambiguous steps. Common data structures and algorithms are analyzed based on their time and space complexity.
3. Data structures can be linear, with sequential elements, or non-linear, with branching elements. Abstract data types define operations on values independently of implementation through inheritance and polymorphism.
Segment trees allow storing interval data and supporting efficient range update and query operations. A segment tree is a binary tree where each node represents an interval. It is constructed by recursively splitting intervals into two halves. This allows updating and querying intervals in O(log n) time. Operations include constructing the tree by inserting values, updating a value, and querying a range by traversing relevant nodes. Segment trees have applications in computational geometry and geographic information systems by supporting fast range minimum/maximum queries.
Given at PyDataSV 2014
In machine learning, clustering is a good way to explore your data and pull out patterns and relationships. Scikit-learn has some great clustering functionality, including the k-means clustering algorithm, which is among the easiest to understand. Let's take an in-depth look at k-means clustering and how to use it. This mini-tutorial/talk will cover what sort of problems k-means clustering is good at solving, how the algorithm works, how to choose k, how to tune the algorithm's parameters, and how to implement it on a set of data.
This document provides an introduction to data structures and algorithms. It defines data as quantities, characters, or symbols operated on by a computer. Data structures are described as organized ways to store and access data efficiently. Common data structures include arrays, linked lists, trees, stacks, and queues. Algorithms are sets of instructions to solve problems, taking input and producing output. Good algorithms are correct, unambiguous, and efficient. Examples demonstrate data structures like arrays and graphs, as well as a simple maximum-finding algorithm. The conclusion emphasizes the importance of data structures.
The document discusses segment trees, which allow querying which stored intervals contain a given point. It presents the basic functions of segment trees - initialization, querying, and updating. Initialization builds the tree by dividing segments into halves and storing sums at each node. Querying returns the sum in a range by checking if a segment is fully within, outside of, or partially within the range. Updating modifies values by adding to all relevant nodes. Segment trees enable efficient range minimum/maximum queries and have numerous applications.
This document discusses stacks and queues as data structures. It begins by defining a stack as a linear collection where elements are added and removed from the top in a last-in, first-out (LIFO) manner. Common stack operations like push, pop, and peek are described. It then discusses applications of stacks like undo sequences and method calls. The document also defines queues as collections where elements are added to the rear and removed from the front in a first-in, first-out (FIFO) manner. Common queue operations and applications like waiting lists and printer access are also covered. Finally, it discusses implementations of stacks and queues using arrays and how to handle overflow and underflow cases.
A segment tree is a data structure that allows for efficient query and update operations on array intervals in logarithmic time. It represents intervals as nodes in a tree structure, where each node corresponds to an interval. Segment trees allow querying the minimum value in a range in O(log n) time and updating values in O(log n) time. The document provides pseudocode for initializing, updating, and querying a segment tree to solve problems involving finding minimum values over ranges.
K-Means, its Variants and its ApplicationsVarad Meru
This presentation was given by our project group at the Lead College competition at Shivaji University. Our project got the 1st Prize. We focused mainly on Rough K-Means and build a Social-Network-Recommender System based on Rough K-Means.
The Members of the Project group were -
Mansi Kulkarni,
Nikhil Ingole,
Prasad Mohite,
Varad Meru
Vishal Bhavsar.
Wonderful Experience !!!
Python for Data Science is a must learn for professionals in the Data Analytics domain. With the growth in IT industry, there is a booming demand for skilled Data Scientists and Python has evolved as the most preferred programming language. Through this blog, you will learn the basics, how to analyze data and then create some beautiful visualizations using Python.
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
A Fast and Dirty Intro to NetworkX (and D3)Lynn Cherny
Using the python lib NetworkX to calculate stats on a Twitter network, and then display the results in several D3.js visualizations. Links to demos and source files. I'm @arnicas and live at www.ghostweather.com.
This document provides a step-by-step guide to learning R. It begins with the basics of R, including downloading and installing R and R Studio, understanding the R environment and basic operations. It then covers R packages, vectors, data frames, scripts, and functions. The second section discusses data handling in R, including importing data from external files like CSV and SAS files, working with datasets, creating new variables, data manipulations, sorting, removing duplicates, and exporting data. The document is intended to guide users through the essential skills needed to work with data in R.
In this tutorial, we explore the most basic data structure in R, the vector. We cover everything from creating vectors to subsetting them in different ways.
This document provides an overview of data structures and algorithms. It introduces common linear data structures like stacks, queues, and linked lists. It discusses the need for abstract data types and different data types. It also covers implementing stacks as a linked list and common stack operations. Key applications of stacks include function call stacks which use a LIFO structure to remember the order of function calls and returns.
This document provides an outline of topics for learning the R programming language, including R basics, vectors and factors, arrays, matrices, lists, data frames, if/else statements, for loops, user defined functions, objects and classes, reading data files, string operations, and regular expressions. Key concepts covered are defining vectors and factors, performing operations on vectors, summarizing data, accessing and manipulating arrays and matrices, the structure and operations of data frames, using if/else statements and for/while loops, defining user functions, detecting object classes and converting between types, reading different file types into R, and using string and regular expression functions.
This document discusses methods in Java. It explains that a method declaration specifies the code that is executed when the method is called. Methods can take parameters as input and can return a value. The document provides an example of a Rectangle class with methods to calculate the area and perimeter of a rectangle. It includes the class definition with these methods and shows how to create Rectangle objects, call the methods on them, and print the results.
Entity Resolution is the task of disambiguating manifestations of real world entities through linking and grouping and is often an essential part of the data wrangling process. There are three primary tasks involved in entity resolution: deduplication, record linkage, and canonicalization; each of which serve to improve data quality by reducing irrelevant or repeated data, joining information from disparate records, and providing a single source of information to perform analytics upon. However, due to data quality issues (misspellings or incorrect data), schema variations in different sources, or simply different representations, entity resolution is not a straightforward process and most ER techniques utilize machine learning and other stochastic approaches.
Machine learning is the hacker art of describing the features of instances that we want to make predictions about, then fitting the data that describes those instances to a model form. Applied machine learning has come a long way from it's beginnings in academia, and with tools like Scikit-Learn, it's easier than ever to generate operational models for a wide variety of applications. Thanks to the ease and variety of the tools in Scikit-Learn, the primary job of the data scientist is model selection. Model selection involves performing feature engineering, hyperparameter tuning, and algorithm selection. These dimensions of machine learning often lead computer scientists towards automatic model selection via optimization (maximization) of a model's evaluation metric. However, the search space is large, and grid search approaches to machine learning can easily lead to failure and frustration. Human intuition is still essential to machine learning, and visual analysis in concert with automatic methods can allow data scientists to steer model selection towards better fitted models, faster. In this talk, we will discuss interactive visual methods for better understanding, steering, and tuning machine learning models.
This document discusses methods and constructors in Java. It defines methods as tasks that can take parameters and return values. Constructors are used to initialize new objects and are called when a new object is created using the new keyword. The document provides an example Rectangle class with methods to calculate area and perimeter, and a constructor to initialize length and width variables. It also shows calling these methods on Rectangle objects after creating them with the constructor.
1. Data structures organize data in memory for efficient access and processing. They represent relationships between data values through placement and linking of the values.
2. Algorithms are finite sets of instructions that take inputs, produce outputs, and terminate after a finite number of unambiguous steps. Common data structures and algorithms are analyzed based on their time and space complexity.
3. Data structures can be linear, with sequential elements, or non-linear, with branching elements. Abstract data types define operations on values independently of implementation through inheritance and polymorphism.
Segment trees allow storing interval data and supporting efficient range update and query operations. A segment tree is a binary tree where each node represents an interval. It is constructed by recursively splitting intervals into two halves. This allows updating and querying intervals in O(log n) time. Operations include constructing the tree by inserting values, updating a value, and querying a range by traversing relevant nodes. Segment trees have applications in computational geometry and geographic information systems by supporting fast range minimum/maximum queries.
Given at PyDataSV 2014
In machine learning, clustering is a good way to explore your data and pull out patterns and relationships. Scikit-learn has some great clustering functionality, including the k-means clustering algorithm, which is among the easiest to understand. Let's take an in-depth look at k-means clustering and how to use it. This mini-tutorial/talk will cover what sort of problems k-means clustering is good at solving, how the algorithm works, how to choose k, how to tune the algorithm's parameters, and how to implement it on a set of data.
This document provides an introduction to data structures and algorithms. It defines data as quantities, characters, or symbols operated on by a computer. Data structures are described as organized ways to store and access data efficiently. Common data structures include arrays, linked lists, trees, stacks, and queues. Algorithms are sets of instructions to solve problems, taking input and producing output. Good algorithms are correct, unambiguous, and efficient. Examples demonstrate data structures like arrays and graphs, as well as a simple maximum-finding algorithm. The conclusion emphasizes the importance of data structures.
The document discusses segment trees, which allow querying which stored intervals contain a given point. It presents the basic functions of segment trees - initialization, querying, and updating. Initialization builds the tree by dividing segments into halves and storing sums at each node. Querying returns the sum in a range by checking if a segment is fully within, outside of, or partially within the range. Updating modifies values by adding to all relevant nodes. Segment trees enable efficient range minimum/maximum queries and have numerous applications.
This document discusses stacks and queues as data structures. It begins by defining a stack as a linear collection where elements are added and removed from the top in a last-in, first-out (LIFO) manner. Common stack operations like push, pop, and peek are described. It then discusses applications of stacks like undo sequences and method calls. The document also defines queues as collections where elements are added to the rear and removed from the front in a first-in, first-out (FIFO) manner. Common queue operations and applications like waiting lists and printer access are also covered. Finally, it discusses implementations of stacks and queues using arrays and how to handle overflow and underflow cases.
A segment tree is a data structure that allows for efficient query and update operations on array intervals in logarithmic time. It represents intervals as nodes in a tree structure, where each node corresponds to an interval. Segment trees allow querying the minimum value in a range in O(log n) time and updating values in O(log n) time. The document provides pseudocode for initializing, updating, and querying a segment tree to solve problems involving finding minimum values over ranges.
K-Means, its Variants and its ApplicationsVarad Meru
This presentation was given by our project group at the Lead College competition at Shivaji University. Our project got the 1st Prize. We focused mainly on Rough K-Means and build a Social-Network-Recommender System based on Rough K-Means.
The Members of the Project group were -
Mansi Kulkarni,
Nikhil Ingole,
Prasad Mohite,
Varad Meru
Vishal Bhavsar.
Wonderful Experience !!!
Python for Data Science is a must learn for professionals in the Data Analytics domain. With the growth in IT industry, there is a booming demand for skilled Data Scientists and Python has evolved as the most preferred programming language. Through this blog, you will learn the basics, how to analyze data and then create some beautiful visualizations using Python.
Best Data Science Ppt using Python
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.
This document provides an overview of popular Python libraries for data science, including NumPy, SciPy, Pandas, Scikit-Learn, matplotlib and Seaborn. It describes what each library is used for, such as NumPy for multidimensional arrays and mathematical operations, Pandas for data manipulation and analysis, and Scikit-Learn for machine learning algorithms. It also discusses reading and exploring data frames, selecting and filtering data, aggregating and grouping data, handling missing values, and data visualization.
This document provides an overview of popular Python libraries for data science and analysis. It discusses NumPy for efficient numerical computations, SciPy for scientific computing functions, Pandas for data structures and analysis, Scikit-Learn for machine learning algorithms, and Matplotlib and Seaborn for data visualization. It also describes common operations in Pandas like reading data, selecting and filtering data, descriptive statistics, and grouping data.
This document provides an overview of popular Python libraries for data science, including NumPy, SciPy, Pandas, Scikit-Learn, matplotlib and Seaborn. It describes the main functions of each library, such as NumPy for multidimensional arrays and mathematical operations, Pandas for data structures and data manipulation, Scikit-Learn for machine learning algorithms, and matplotlib and Seaborn for data visualization. The document also covers reading and exploring data frames, selecting and filtering data, aggregating and grouping data, handling missing values, and basic statistical analysis and graphics.
This document provides an overview of popular Python libraries for data science and analysis. It discusses NumPy for efficient numerical computations, SciPy for scientific computing functions, Pandas for data structures and manipulation, Scikit-Learn for machine learning algorithms, and Matplotlib and Seaborn for data visualization. It also describes common operations in Pandas like reading data, exploring data frames, selecting columns and rows, filtering, grouping, and descriptive statistics.
This document describes a course on data structures and algorithms. The course covers fundamental algorithms like sorting and searching as well as data structures including arrays, linked lists, stacks, queues, trees, and graphs. Students will learn to analyze algorithms for efficiency, apply techniques like recursion and induction, and complete programming assignments implementing various data structures and algorithms. The course aims to enhance students' skills in algorithm design, implementation, and complexity analysis. It is worth 4 credits and has prerequisites in computer programming. Student work will be graded based on assignments, exams, attendance, and a final exam.
- Exploratory data analysis (EDA) is used to summarize and visualize data to understand its key characteristics, variables, and relationships.
- In R, EDA involves descriptive statistics like mean, median, and mode as well as graphical methods like histograms, density plots, and box plots.
- Functions like head(), tail(), summary(), and str() provide information about the structure, dimensions, and descriptive statistics of data frames in R. Additional functions like pairs plots and faceted histograms allow visualizing relationships between variables.
This document discusses Apache Spark machine learning (ML) workflows for recommendation systems using collaborative filtering. It describes loading rating data from users on items into a DataFrame and splitting it into training and test sets. An ALS model is fit on the training data and used to make predictions on the test set. The root mean squared error is calculated to evaluate prediction accuracy.
The document provides an overview of popular Python libraries for data science such as NumPy, SciPy, Pandas, SciKit-Learn, matplotlib, and Seaborn. It discusses the key features and uses of each library. The document also demonstrates how to load data into Pandas data frames, explore and manipulate the data frames using various methods like head(), groupby(), filtering, and slicing. Summary statistics, plotting and other analyses can be performed on the data frames using these libraries.
The document discusses various Python libraries used for data science tasks. It describes NumPy for numerical computing, SciPy for algorithms, Pandas for data structures and analysis, Scikit-Learn for machine learning, Matplotlib for visualization, and Seaborn which builds on Matplotlib. It also provides examples of loading data frames in Pandas, exploring and manipulating data, grouping and aggregating data, filtering, sorting, and handling missing values.
fINAL Lesson_5_Data_Manipulation_using_R_v1.pptxdataKarthik
Anna is a junior data scientist working on a customer retention strategy. She needs to analyze data from different sources to understand customer value. To efficiently perform her job, she needs to learn techniques for reading, merging, summarizing and preparing data for analysis in R. These include reading data from files and databases, merging tables, summarizing data using functions like mean, median, and aggregate, and exporting cleaned data.
Document clustering for forensic analysis an approach for improving compute...Madan Golla
The document proposes an approach to apply document clustering algorithms to forensic analysis of computers seized in police investigations. It discusses using six representative clustering algorithms - K-means, K-medoids, Single/Complete/Average Link hierarchical clustering, and CSPA ensemble clustering. The approach estimates the number of clusters automatically from the data using validity indexes like silhouette, in order to facilitate computer inspection and speed up the analysis process compared to examining each document individually.
- Hierarchical clustering is an unsupervised machine learning algorithm that groups unlabeled datasets into a hierarchical structure of clusters. It does not require predetermining the number of clusters as in K-means clustering.
- There are two approaches: agglomerative (bottom-up) and divisive (top-down). Agglomerative clustering starts with each data point as a cluster and merges them iteratively, while divisive clustering splits clusters iteratively.
- Hierarchical clustering represents the clustering hierarchy as a dendrogram tree structure. The optimal number of clusters can be determined by cutting the dendrogram tree at an appropriate level.
This document provides an overview of a machine learning course that teaches Pandas basics. The course aims to teach students how to handle and visualize data, apply basic learning algorithms, develop supervised and unsupervised learning techniques, and build machine learning models. The document outlines the course objectives, outcomes, syllabus including data preprocessing, feature extraction, and data visualization techniques. It also provides references for further reading.
The document describes developing a model to predict house prices using deep learning techniques. It proposes using a dataset with house features without labels and applying regression algorithms like K-nearest neighbors, support vector machine, and artificial neural networks. The models are trained and tested on split data, with the artificial neural network achieving the lowest mean absolute percentage error of 18.3%, indicating it is the most accurate model for predicting house prices based on the data.
What am I going to get from this course?
Provides a basic conceptual understanding of how clustering works
Provides intuitive understanding of the mathematics behind various clustering algorithms
Walk through Python code examples on how to use various cluster algorithms
Show how clustering is applied in various industry applications
Check it on Experfy: https://www.experfy.com/training/courses/unsupervised-learning-clustering
"Optimization of a .NET application- is it simple ! / ?", Yevhen TatarynovFwdays
Optimization of .NET application seems complex and tied full task, but don’t hurry up with conclusions. Let’s look on several cases from real projects.
For this we:
look under the hood of an application from a real project;
define the metric for optimization;
choose the necessary tools;
find bottlenecks /memory leaks and best practice to resolve them.
We'll improve the application step by step and we’ll what with simple analysis and simple best practice we can significantly reduce total resources usage.
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
Consistent toolbox talks are critical for maintaining workplace safety, as they provide regular opportunities to address specific hazards and reinforce safe practices.
These brief, focused sessions ensure that safety is a continual conversation rather than a one-time event, which helps keep safety protocols fresh in employees' minds. Studies have shown that shorter, more frequent training sessions are more effective for retention and behavior change compared to longer, infrequent sessions.
Engaging workers regularly, toolbox talks promote a culture of safety, empower employees to voice concerns, and ultimately reduce the likelihood of accidents and injuries on site.
The traditional method of conducting safety talks with paper documents and lengthy meetings is not only time-consuming but also less effective. Manual tracking of attendance and compliance is prone to errors and inconsistencies, leading to gaps in safety communication and potential non-compliance with OSHA regulations. Switching to a digital solution like Safelyio offers significant advantages.
Safelyio automates the delivery and documentation of safety talks, ensuring consistency and accessibility. The microlearning approach breaks down complex safety protocols into manageable, bite-sized pieces, making it easier for employees to absorb and retain information.
This method minimizes disruptions to work schedules, eliminates the hassle of paperwork, and ensures that all safety communications are tracked and recorded accurately. Ultimately, using a digital platform like Safelyio enhances engagement, compliance, and overall safety performance on site. https://safelyio.com/
Using Query Store in Azure PostgreSQL to Understand Query PerformanceGrant Fritchey
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...XfilesPro
Wondering how X-Sign gained popularity in a quick time span? This eSign functionality of XfilesPro DocuPrime has many advancements to offer for Salesforce users. Explore them now!
Why Apache Kafka Clusters Are Like Galaxies (And Other Cosmic Kafka Quandarie...Paul Brebner
Closing talk for the Performance Engineering track at Community Over Code EU (Bratislava, Slovakia, June 5 2024) https://eu.communityovercode.org/sessions/2024/why-apache-kafka-clusters-are-like-galaxies-and-other-cosmic-kafka-quandaries-explored/ Instaclustr (now part of NetApp) manages 100s of Apache Kafka clusters of many different sizes, for a variety of use cases and customers. For the last 7 years I’ve been focused outwardly on exploring Kafka application development challenges, but recently I decided to look inward and see what I could discover about the performance, scalability and resource characteristics of the Kafka clusters themselves. Using a suite of Performance Engineering techniques, I will reveal some surprising discoveries about cosmic Kafka mysteries in our data centres, related to: cluster sizes and distribution (using Zipf’s Law), horizontal vs. vertical scalability, and predicting Kafka performance using metrics, modelling and regression techniques. These insights are relevant to Kafka developers and operators.
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
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14 th Edition of International conference on computer visionShulagnaSarkar2
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14th Edition of International conference on computer vision
Computer conferences organized by ScienceFather group. ScienceFather takes the privilege to invite speakers participants students delegates and exhibitors from across the globe to its International Conference on computer conferences to be held in the Various Beautiful cites of the world. computer conferences are a discussion of common Inventions-related issues and additionally trade information share proof thoughts and insight into advanced developments in the science inventions service system. New technology may create many materials and devices with a vast range of applications such as in Science medicine electronics biomaterials energy production and consumer products.
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1. Users categorization of StackOverflow data
Using K-Means clustering Algorithm
Project Presentation
Team Membar – Afzal Ahmad and Abhishek Barnwal
2. What is StackOverflow ?
• Stack Overflow is a question and answer site
Written in C# for professional and enthusiast
programmers. It's built and run by us as part of
the Stack Exchange network of Q&A sites.
3. About User Account on stackoverflow
• This site is all about getting answers. Good answers are voted up and
rise to the top .
• User reputation score goes up when others vote up his questions,
answers and edits.
• Badges are special achievements User earns for participating on the
site. They come in three levels: bronze, silver, and gold.
• The person who asked can mark one answer as "accepted".
4. DataSet Overview
• The dataset is obtained from stackexchange data dump at the
internet archieve.
• The link to the dataset is as follows.
Www.archive.org/details/stackexchange
•Each site under stack exchange is formatted as a separate archive
Consisting of xml file zipped via 7-zip that includes various files.
5.
6. Dataset overview
• Stack overflow dataset consists of following files that is treated as table in
our database design.
1.posts
2.postLinks
3.Tags
4.Users
5.Votes
6.Badges
7.Comments
♥ But we are interested only in Users file which contains user's Id and and his
features like age,reputation,upotes,downvotes etc...
7.
8. Features of Users Data
1. Age
2. Reputations
3. Upvotes
4. Downvotes
5. Views
9. Data preprocessing
• Our Dataset is in XML format and unfit for our algorithm to process
that’s why we need data processing to make it fit for our algorithm to
process it.
• Data preprocessing is a data mining technique that involves
transforming raw data into an understandable format.
• To achieve tha data in desired format we need to parse it.
10. python script to convert xml to csv
from copy import deepcopy
import numpy as np
import pandas as pd
#from matplotlib import pyplot as plt
#%matplotlib inline
#plt.rcParams['figure.figsize'] = (16, 9)
#plt.style.use('ggplot')
import xml.etree.ElementTree as ET
import csv
11. python script to convert xml to csv
tree = ET.parse("Users.xml")
root = tree.getroot()
# open a file for writing
User_data = open('user_data1.csv', 'w')
# create the csv writer object
csvwriter = csv.writer(User_data)
count = 0
12. python script to convert xml to csv
csvwriter.writerow(['Reputation', 'Views', 'UpVotes', 'DownVotes', 'Age'])
for i in root.findall('row'):
data = [i.get('Reputation'), i.get('Views'), i.get('UpVotes'), i.get('DownVotes'), i.get('Age') or '0']
# print data
count = count + 1
csvwriter.writerow(data)
User_data.close()
14. What is clustering ?
Clustering is the task of dividing the population or data points
into a number of groups such that data points in the same groups
are more similar to other data points in the same group than
those in other groups. In simple words, the aim is to segregate
groups with similar traits and assign them into clusters.
16. Types of Clustering
1. Hard Clustering: In hard clustering, each data point either
belongs to a cluster completely or not.
2. Soft Clustering: In soft clustering, instead of putting each
data point into a separate cluster, a probability or
likelihood of that data point to be in those clusters is
assigned.
17. Algorithm Used
• We are using K-means clustering algorithm to categorise the user of
different types on the basis of given features.
• k-means clustering is a data mining/machine learning algorithm used
to cluster observations into groups of related observations without
any prior knowledge of those relationships.
• This algorithm is also called unsupervised learning algorithm as it
does not have any idea of label of cluster.
• Using this algorithm we find the different k -categories depending on
the value of K.
18. Unsupervised Learning
Unsupervised learning is a type of machine learning algorithm used to
draw inferences from datasets consisting of input data without labeled
responses.
The most common unsupervised learning method is cluster analysis, which
is used for exploratory data analysis to find hidden patterns or grouping in
data. The clusters are modeled using a measure of similarity which is
defined upon metrics such as Euclidean or probabilistic distance.
19. Working of K-Means Algorithm
1 .Specify the desired number of clusters K : Let us choose k=2 for
these 5 data points in 2-D space.
20. 2 . Randomly assign each data point to a cluster : Let’s assign three
points in cluster 1 shown using red color and two points in cluster 2
shown using grey color.
21. 3 . Compute cluster centroids : The centroid of data points in the red
cluster is shown using red cross and those in grey cluster using grey
cross.
23. 5. Re-compute cluster centroids : Now, re-computing the
centroids for both the clusters.
24. 6. Repeat steps 4 and 5 until no improvements are possible.
When there will be no further switching of data points between two
clusters for two successive repeats. It will mark the termination of the
algorithm if not explicitly mentioned.
26. Implementation of K-means Algorithm
1. We have converted our XML data into CSV.
2. Run K-Means Algorithm on stackoverflow data.
3. If K=4 then We get the four cluster center with the values given
below.
array([[ 1.82709702e+02, 8.86936593e-01, 8.58670741e-01,
3.59052712e-02, 3.21581360e+01],
[ 1.71912000e+04, 7.34000000e+01, 1.92800000e+02,
1.29000000e+01, 3.92000000e+01],
[ 3.89650000e+04, 3.47000000e+02, 5.10000000e+02,
8.60000000e+01, 3.00000000e+01],
[ 4.18018750e+03, 1.38750000e+01, 3.42187500e+01,
1.40625000e+00, 3.27187500e+01]])
28. Important information regarding insights of
data
1.We processed the data of android users of stack overflow.
2.Here all the results and insights are only of android specific users.
3.We used only numerical value information of User’s as K-Means
algorithm works on Euclidean distance.
4. User’s information used here are as follows.
‘Age’ , ‘Views’ ,’Reputations’, ‘Upvotes’, Downvotes
29. Insights from stack overflow data
1. Almost all the users of android specific are above 30 in Age.
2. Users who have maximum reputations,views,upvotes and
downvotes are of minimum age among all other users.It means
young community is more involved in android than older.
3. With the growth of Age users are not interested to downvote the
answer. Young community is most involved in downvoting as well as
in upvoting to the answer.
4. Profile views are mostly affected by reputation.It is increasing 3-4
times on doubling the reputation.