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Top 8 Data Mining Techniques In Machine Learning

#artificialintelligence

Data mining is considered to be one of the popular terms of machine learning as it extracts meaningful information from the large pile of datasets and is used for decision-making tasks. It is a technique to identify patterns in a pre-built database and is used quite extensively by organisations as well as academia. The various aspects of data mining include data cleaning, data integration, data transformation, data discretisation, pattern evaluation and more. Below, we have listed the top eight data mining techniques in machine learning that is most used by data scientists. Association Rule Learning is one of the unsupervised data mining techniques in which an item set is defined as a collection of one or more items.


Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing

arXiv.org Artificial Intelligence

The implementation of robust, stable, and user-centered data analytics and machine learning models is confronted by numerous challenges in production and manufacturing. Therefore, a systematic approach is required to develop, evaluate, and deploy such models. The data-driven knowledge discovery framework provides an orderly partition of the data-mining processes to ensure the practical implementation of data analytics and machine learning models. However, the practical application of robust industry-specific data-driven knowledge discovery models faces multiple data-- and model-development--related issues. These issues should be carefully addressed by allowing a flexible, customized, and industry-specific knowledge discovery framework; in our case, this takes the form of the cross-industry standard process for data mining (CRISP-DM). This framework is designed to ensure active cooperation between different phases to adequately address data- and model-related issues. In this paper, we review several extensions of CRISP-DM models and various data-robustness-- and model-robustness--related problems in machine learning, which currently lacks proper cooperation between data experts and business experts because of the limitations of data-driven knowledge discovery models.


Data Mining and Machine Learning: Fundamental Concepts and Algorithms: The Free eBook - KDnuggets

#artificialintelligence

We are pleased to announce the second edition of our book Data Mining and Machine Learning: Fundamental Concepts and Algorithms, Second Edition, by Mohammed J. Zaki and Wagner Meira, Jr., published by Cambridge University Press, 2020. The entire book is available to read online for free and the site includes video lectures and other resources. New to this edition is an entire part devoted to regression and deep learning. The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners.


Global Big Data Conference

#artificialintelligence

When you think of the words "data" and "mine", no doubt the idea of data mining comes first. However, just as much as we find value in mining the rich resources of data, so too can we apply the advanced techniques for dealing with data to real-world mining -- that is, extracting natural resources from the earth. The world is just as dependent on natural resources as it is data resources, so it makes sense to see how the evolving areas of artificial intelligence and machine learning have an impact on the world of mining and natural resource extraction. Mining has always been a dangerous profession, since extracting minerals, natural gas, petroleum, and other resources requires working in conditions that can be dangerous for human life. Increasingly, we are needing to go to harsher climates such as deep under the ocean or deep inside the earth to extract the resources we still need.


AI And The Digital Mine

#artificialintelligence

When you think of the words "data" and "mine", no doubt the idea of data mining comes first. However, just as much as we find value in mining the rich resources of data, so too can we apply the advanced techniques for dealing with data to real-world mining -- that is, extracting natural resources from the earth. The world is just as dependent on natural resources as it is data resources, so it makes sense to see how the evolving areas of artificial intelligence and machine learning have an impact on the world of mining and natural resource extraction. Mining has always been a dangerous profession, since extracting minerals, natural gas, petroleum, and other resources requires working in conditions that can be dangerous for human life. Increasingly, we are needing to go to harsher climates such as deep under the ocean or deep inside the earth to extract the resources we still need.


Difference Between Data Mining, Machine Learning and Big Data

#artificialintelligence

The amount of digital data that currently exists is now growing at a rapid pace. The number is doubling every two years and it is completely transforming our basic mode of existence. According to a paper from IBM, about 2.5 billion gigabytes of data had been generated on a daily basis in the year 2012. Another article from Forbes informs us that data is growing at a pace which is faster than ever. The same article suggests that this year, 2020, about 1.7 billion of new information will be developed per second for all the human inhabitants on this planet.


6 Process Excellence Trends to watch out for in 2020

#artificialintelligence

New technologies like artificial intelligence and machine learning are changing the way work gets done all over the world. We believe that 2020 is the year that companies will embrace these powerful technologies and apply them to revolutionize their business processes. Here's how process-minded leaders can capture the opportunity. In the past few years, Process Mining grew faster than any other technology in the BPM and process excellence space -- even faster than RPA, according to theInternational Data Corporation, IDC. In 2020, the rapid growth will continue.


Business Problems and Data Science Solutions Part 1

#artificialintelligence

An important principle of data science is that data mining is a process. It includes the application of information technology, such as the automated discovery and evaluation of patterns from data. It also includes an analyst's creativity, business knowledge, and common sense. Understanding the whole process helps to structure data mining projects. Since the data mining process breaks up the overall task of finding patterns from data into a set of well-defined subtasks, it is also useful for structuring discussions about data science.


Understanding MLOps with Azure Databricks

#artificialintelligence

As I've been focusing more and more on the Big Data and Machine Learning ecosystem, I've found Azure Databricks to be an elegant, powerful and intuitive part of the Azure Data offerings. Over my last 12 months at Slalom, I have had the incredible opportunity to travel across Canada and work hand in hand with the brilliant folks at Microsoft's Data & AI practice and Databricks experts to lead project engagements, deliver technical hands-on workshops, listen to the industry experts - the folks doing Data Science for a full time living - and absorb everything in between. There's a common theme across the industry verticals that's going to be our point of discussion today. The hot topic of 21st century tech is Machine Learning - some flavor of AI/ML is thrown into almost everything we find these days (I'm pretty sure I spotted a "genius" AI/ML toothbrush at Shoppers Drug Mart today). The reality is, the mathematical techniques that power Machine Learning models have been around for almost a century.


Why CIOs need to adopt a process mining initiative

#artificialintelligence

The field of process mining started in the late 1990s when Wil van der Aalst, who is now a professor leading the Process and Data Science group at RWTH Aachen University, began looking for ways to combine process science and data science. Much of this early work was theoretical, but the field has started accelerating over the last couple of year with advancements in data gathering and analytics technologies. "The adoption of process mining has accelerated over the last couple of years," van der Aalst said in an interview. There are now over 30 vendors of commercial process mining tools, including leaders like Celonis, Disco, UiPath (ProcessGold), myInvenio, Minit, Mehrwerk, Lana Labs, StereoLOGIC and Everflow. This has made it easier for large organizations, like Siemens and BMW, to apply process mining at scale with thousands of process mining users.