Data mining involves sorting through large datasets to identify patterns and relationships. It is used to predict future trends through data analysis. The goal of data mining is extracting patterns from data, not extracting the data itself. It is an interdisciplinary field that uses computer science and statistics to extract useful information from datasets. Data mining is part of the knowledge discovery in databases (KDD) process, which involves data preparation, cleansing, modeling, and interpreting results to extract useful knowledge from data. The difference between data mining and data analysis is that data analysis summarizes past data while data mining focuses on using models to predict the future.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
Decision tree induction \ Decision Tree Algorithm with Example| Data scienceMaryamRehman6
This Decision Tree Algorithm in Machine Learning Presentation will help you understand all the basics of Decision Tree along with what Machine Learning is, what Machine Learning is, what Decision Tree is, the advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with resolved examples, and at the end of the decision Tree use case/demo in Python for loan payment. For both beginners and experts who want to learn Machine Learning Algorithms, this Decision Tree tutorial is perfect.
BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data-mining algorithm used to perform hierarchical clustering over, particularly large data sets.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
Decision tree induction \ Decision Tree Algorithm with Example| Data scienceMaryamRehman6
This Decision Tree Algorithm in Machine Learning Presentation will help you understand all the basics of Decision Tree along with what Machine Learning is, what Machine Learning is, what Decision Tree is, the advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with resolved examples, and at the end of the decision Tree use case/demo in Python for loan payment. For both beginners and experts who want to learn Machine Learning Algorithms, this Decision Tree tutorial is perfect.
BIRCH (balanced iterative reducing and clustering using hierarchies) is an unsupervised data-mining algorithm used to perform hierarchical clustering over, particularly large data sets.
This lecture gives various definitions of Data Mining. It also gives why Data Mining is required. Various examples on Classification , Cluster and Association rules are given.
Introduction to Web Mining and Spatial Data MiningAarshDhokai
Data Ware Housing And Mining subject offer in Gujarat Technological University in Branch of Information and Technology.
This Topic is from chapter 8 named Advance Topics.
Seminar Presentation | Network Intrusion Detection using Supervised Machine L...Jowin John Chemban
By:
Jowin John Chemban (jowinchemban@gmail.com)
HGW16CS022 (2016-2020 Batch)
S7 B.Tech Computer Science Engineering
Holy Grace Academy of Engineering, Mala
Date : September 2019
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
Data preprocessing techniques are applied before mining. These can improve the overall quality of the patterns mined and the time required for the actual mining.
Some important data preprocessing that must be needed before applying the data mining algorithm to any data sets are completely described in these slides.
We are living in a world, where a vast amount of digital data which is called big data. Plus as the world becomes more and more connected via the Internet of Things (IoT). The IoT has been a major influence on the Big Data landscape. The analysis of such big data brings ahead business competition to the next level of innovation and productivity.
This lecture gives various definitions of Data Mining. It also gives why Data Mining is required. Various examples on Classification , Cluster and Association rules are given.
Introduction to Web Mining and Spatial Data MiningAarshDhokai
Data Ware Housing And Mining subject offer in Gujarat Technological University in Branch of Information and Technology.
This Topic is from chapter 8 named Advance Topics.
Seminar Presentation | Network Intrusion Detection using Supervised Machine L...Jowin John Chemban
By:
Jowin John Chemban (jowinchemban@gmail.com)
HGW16CS022 (2016-2020 Batch)
S7 B.Tech Computer Science Engineering
Holy Grace Academy of Engineering, Mala
Date : September 2019
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
Data preprocessing techniques are applied before mining. These can improve the overall quality of the patterns mined and the time required for the actual mining.
Some important data preprocessing that must be needed before applying the data mining algorithm to any data sets are completely described in these slides.
We are living in a world, where a vast amount of digital data which is called big data. Plus as the world becomes more and more connected via the Internet of Things (IoT). The IoT has been a major influence on the Big Data landscape. The analysis of such big data brings ahead business competition to the next level of innovation and productivity.
DATA MINING IN EDUCATION : A REVIEW ON THE KNOWLEDGE DISCOVERY PERSPECTIVEIJDKP
Knowledge Discovery in Databases is the process of finding knowledge in massive amount of data where
data mining is the core of this process. Data mining can be used to mine understandable meaningful patterns from large databases and these patterns may then be converted into knowledge.Data mining is the process of extracting the information and patterns derived by the KDD process which helps in crucial decision-making.Data mining works with data warehouse and the whole process is divded into action plan to be performed on data: Selection, transformation, mining and results interpretation. In this paper, we have reviewed Knowledge Discovery perspective in Data Mining and consolidated different areas of data
mining, its techniques and methods in it.
What Is Data Mining How It Works, Benefits, Techniques.pdfAgile dock
Want to understand data mining better? Read our file for a breakdown of techniques like classification and clustering. Start extracting actionable insights today.
Data Mining – Definition, Challenges, tasks, Data pre-processing, Data Cleaning, missing data, dimensionality reduction, data transformation, measures of similarity and dissimilarity, Introduction to Association rules, APRIORI algorithm, partition algorithm, FP growth algorithm, Introduction to Classification techniques, Decision tree, Naïve-Bayes classifier, k-nearest neighbour, classification algorithm.
INTRODUCTION TO DATA MINING
This word document contain the notes of data mining. It tells the basics of data mining like what is Data mining, it's types, issues, advantages, disadvantages, applications, social implications, basis tasks and KDD process etc. While making this notes, I had taken help from different websites of google.
Uncover Trends and Patterns with Data Science.pdfUncodemy
In today's data-driven world, the vast amount of information generated every second presents both challenges and opportunities for businesses and researchers alike. Harnessing this data effectively can provide valuable insights, unlock hidden trends, and identify patterns that drive innovation and strategic decision-making.
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
2. DATA MINING(DEFINITION)
Data mining is the process of sorting through
large data sets to identify patterns and establish
relationships to solve problems through data
analysis. Data mining tools allow enterprises to
predict future trends.
The term "data mining" is in fact a misnomer,
because the goal is the extraction of patterns
and knowledge from large amounts of data, not
the extraction (mining) of data itself.
3. Data mining is an interdisciplinary subfield
of computer science and statistics with an
overall goal to extract information (with
intelligent methods) from a data set and
transform the information into a
comprehensible structure for further use. Data
mining is the analysis step of the "knowledge
discovery in databases" process, or KDD.
4. Aside from the raw analysis step, it also
involves database and data
management aspects, data pre-
processing, model and inference considerations
, interestingness
metrics, complexity considerations, post-
processing of discovered
structures, visualization, and online updating.
5. The difference between data analysis and data
mining is that data analysis is to summarize the
history such as analyzing the effectiveness of a
marketing campaign, in contrast, data mining
focuses on using specific machine learning and
statistical models to predict the future and
discover the patterns among data.
6. Knowledge Discovery in Databases (KDD)
Knowledge discovery in databases (KDD) is the
process of discovering useful knowledge from a
collection of data. This widely used data mining
technique is a process that includes data preparation
and selection, data cleansing, incorporating prior
knowledge on data sets and interpreting accurate
solutions from the observed results.
Major KDD application areas include marketing,
fraud detection, telecommunication and
manufacturing.
7. Traditionally, data mining and knowledge discovery
was performed manually. As time passed, the amount
of data in many systems grew to larger than terabyte
size, and could no longer be maintained manually.
Moreover, for the successful existence of any
business, discovering underlying patterns in data is
considered essential. As a result, several software
tools were developed to discover hidden data and
make assumptions, which formed a part of artificial
intelligence.
8. The KDD process has reached its peak in the
last 10 years. It now houses many different
approaches to discovery, which includes
inductive learning, Bayesian statistics,
semantic query optimization, knowledge
acquisition for expert systems and information
theory. The ultimate goal is to extract high-
level knowledge from low-level data.
11. STAGES IN KDD:
The overall process of finding and interpreting
patterns from data involves the repeated application of
the following steps:
Developing an understanding of
the application domain
the relevant prior knowledge
the goals of the end-user
12. Creating a target data set: selecting a data set, or
focusing on a subset of variables, or data samples, on
which discovery is to be performed.
Data cleaning and preprocessing.
Removal of noise or outliers.
Collecting necessary information to model or account
for noise.
Strategies for handling missing data fields.
Accounting for time sequence information and known
changes.
13. Data reduction and projection.
Finding useful features to represent the data depending
on the goal of the task.
Using dimensionality reduction or transformation
methods to reduce the effective number of variables
under consideration or to find invariant representations
for the data.
Choosing the data mining task.
Deciding whether the goal of the KDD process is
classification, regression, clustering, etc.
14. Choosing the data mining algorithm(s).
Selecting method(s) to be used for searching for
patterns in the data.
Deciding which models and parameters may be
appropriate.
Matching a particular data mining method with the
overall criteria of the KDD process.
15. Data mining.
Searching for patterns of interest in a particular
representational form or a set of such representations as
classification rules or trees, regression, clustering, and
so forth.
Interpreting mined patterns.
Consolidating discovered knowledge.
