Data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner.
A simulated decision trees algorithm (sdt)Mona Nasr
The customer's information contained in
databases has increased dramatically in the last few years.
Data mining is a good approach to deal with this volume of
information to enhance the process of customer services.
One of the most important and powerful techniques of data
mining is decision trees algorithm. It appropriate for large
and sophisticated business area but it's complicated, high
cost and not easy to use by not specialists in the field. To
overcome this problem SDT is proposed which is a simple,
powerful and low-cost proposed methodology to simulate the
decision trees algorithm for different business scopes and
nature. SDT methodology consists of three phases. The first
phase is the data preparation which prepare data for
computing calculations, the second phase is SDT algorithm
which represents a simulation of decision trees algorithm to
find the most important rules that distinguish specific type of
customers, the third phase is to visualize results and rules for
better understanding and clarifying the results. In this paper
SDT methodology is tested by a dataset consists of 1000
instants for German Credit Data belongs to one of German
bank customers. SDT selects the most important rules and
paths that reaches the selected ratio and tested cluster of
customers successfully with interesting remarks and finding.
A simulated decision trees algorithm (sdt)Mona Nasr
The customer's information contained in
databases has increased dramatically in the last few years.
Data mining is a good approach to deal with this volume of
information to enhance the process of customer services.
One of the most important and powerful techniques of data
mining is decision trees algorithm. It appropriate for large
and sophisticated business area but it's complicated, high
cost and not easy to use by not specialists in the field. To
overcome this problem SDT is proposed which is a simple,
powerful and low-cost proposed methodology to simulate the
decision trees algorithm for different business scopes and
nature. SDT methodology consists of three phases. The first
phase is the data preparation which prepare data for
computing calculations, the second phase is SDT algorithm
which represents a simulation of decision trees algorithm to
find the most important rules that distinguish specific type of
customers, the third phase is to visualize results and rules for
better understanding and clarifying the results. In this paper
SDT methodology is tested by a dataset consists of 1000
instants for German Credit Data belongs to one of German
bank customers. SDT selects the most important rules and
paths that reaches the selected ratio and tested cluster of
customers successfully with interesting remarks and finding.
EXPLORING DATA MINING TECHNIQUES AND ITS APPLICATIONSeditorijettcs
Dr.T.Hemalatha#1, Dr.G.Rashita Banu#2, Dr.Murtaza Ali#3
#1.Assisstant.Professor,VelsUniversity,Chennai
#2Assistant Professor,Department of HIM&T,JazanUniversity,Jasan
#3HOD, Department of HIM&T JazanUniversity,Jasan
Recommendation system using bloom filter in mapreduceIJDKP
Many clients like to use the Web to discover product details in the form of online reviews. The reviews are
provided by other clients and specialists. Recommender systems provide an important response to the
information overload problem as it presents users more practical and personalized information facilities.
Collaborative filtering methods are vital component in recommender systems as they generate high-quality
recommendations by influencing the likings of society of similar users. The collaborative filtering method
has assumption that people having same tastes choose the same items. The conventional collaborative
filtering system has drawbacks as sparse data problem & lack of scalability. A new recommender system is
required to deal with the sparse data problem & produce high quality recommendations in large scale
mobile environment. MapReduce is a programming model which is widely used for large-scale data
analysis. The described algorithm of recommendation mechanism for mobile commerce is user based
collaborative filtering using MapReduce which reduces scalability problem in conventional CF system.
One of the essential operations for the data analysis is join operation. But MapReduce is not very
competent to execute the join operation as it always uses all records in the datasets where only small
fraction of datasets are applicable for the join operation. This problem can be reduced by applying
bloomjoin algorithm. The bloom filters are constructed and used to filter out redundant intermediate
records. The proposed algorithm using bloom filter will reduce the number of intermediate results and will
improve the join performance.
Introduction
Domain Expert
Goal identification and Data Understanding
Data Cleaning
Missing values
Noisy Data
Inconsistent Data
Data Integration
Data Transformation
Data Reduction
Feature Selection
Sampling Discretization
A collection of conceptual tools for describing
data
data relationships
data semantics
data constraints
Relational model
Entity-Relationship model
Other models:
object-oriented model
semi-structured data models
Older models: network model and hierarchical model
Definition, Types of Biometric Identifiers, Factors in Biometrics Systems, Benefits, Criteria for selection of biometrics.
You can find a .PDF document that explains the entire presentation here : https://www.sugarsync.com/pf/D1925164_77526841_812014
Good luck!!
I am Adoitya Kaila .a student of management.here I am presnting a presentation on biometric technology which is considered the most reliable source of security in todays time.i have tried to make it simple for each and everyone .
Technology that identifies you based on your physical or behavioral traits- for added security to confirm that you are who you claim to be.(this ppt is very dear to me as i have given a talk on this topic twice. this also fetched me and migmar first prize at deen dayal upadhyay college- converging vectors - an inter college presentation competition organized by arya bhata science forum)
EXPLORING DATA MINING TECHNIQUES AND ITS APPLICATIONSeditorijettcs
Dr.T.Hemalatha#1, Dr.G.Rashita Banu#2, Dr.Murtaza Ali#3
#1.Assisstant.Professor,VelsUniversity,Chennai
#2Assistant Professor,Department of HIM&T,JazanUniversity,Jasan
#3HOD, Department of HIM&T JazanUniversity,Jasan
Recommendation system using bloom filter in mapreduceIJDKP
Many clients like to use the Web to discover product details in the form of online reviews. The reviews are
provided by other clients and specialists. Recommender systems provide an important response to the
information overload problem as it presents users more practical and personalized information facilities.
Collaborative filtering methods are vital component in recommender systems as they generate high-quality
recommendations by influencing the likings of society of similar users. The collaborative filtering method
has assumption that people having same tastes choose the same items. The conventional collaborative
filtering system has drawbacks as sparse data problem & lack of scalability. A new recommender system is
required to deal with the sparse data problem & produce high quality recommendations in large scale
mobile environment. MapReduce is a programming model which is widely used for large-scale data
analysis. The described algorithm of recommendation mechanism for mobile commerce is user based
collaborative filtering using MapReduce which reduces scalability problem in conventional CF system.
One of the essential operations for the data analysis is join operation. But MapReduce is not very
competent to execute the join operation as it always uses all records in the datasets where only small
fraction of datasets are applicable for the join operation. This problem can be reduced by applying
bloomjoin algorithm. The bloom filters are constructed and used to filter out redundant intermediate
records. The proposed algorithm using bloom filter will reduce the number of intermediate results and will
improve the join performance.
Introduction
Domain Expert
Goal identification and Data Understanding
Data Cleaning
Missing values
Noisy Data
Inconsistent Data
Data Integration
Data Transformation
Data Reduction
Feature Selection
Sampling Discretization
A collection of conceptual tools for describing
data
data relationships
data semantics
data constraints
Relational model
Entity-Relationship model
Other models:
object-oriented model
semi-structured data models
Older models: network model and hierarchical model
Definition, Types of Biometric Identifiers, Factors in Biometrics Systems, Benefits, Criteria for selection of biometrics.
You can find a .PDF document that explains the entire presentation here : https://www.sugarsync.com/pf/D1925164_77526841_812014
Good luck!!
I am Adoitya Kaila .a student of management.here I am presnting a presentation on biometric technology which is considered the most reliable source of security in todays time.i have tried to make it simple for each and everyone .
Technology that identifies you based on your physical or behavioral traits- for added security to confirm that you are who you claim to be.(this ppt is very dear to me as i have given a talk on this topic twice. this also fetched me and migmar first prize at deen dayal upadhyay college- converging vectors - an inter college presentation competition organized by arya bhata science forum)
EXPLORING DATA MINING TECHNIQUES AND ITS APPLICATIONSeditorijettcs
Dr.T.Hemalatha#1, Dr.G.Rashita Banu#2, Dr.Murtaza Ali#3
#1.Assisstant.Professor,VelsUniversity,Chennai
#2Assistant Professor,Department of HIM&T,JazanUniversity,Jasan
#3HOD, Department of HIM&T JazanUniversity,Jasan
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.
Running Head Data Mining in The Cloud .docxhealdkathaleen
Running Head: Data Mining in The Cloud 1
Data Mining in The Cloud 13
Data Mining in The Cloud
Student’s Name:
Institution:
Instructor:
Big data mining on the cloud
Big data mining techniques
Abstract.
Management and analysis of data is becoming a nightmare in every organization day by day. This is because there is flooding of data. This data can only be analyzed by using Information Governance and big data mining techniques. This paper aims to look at some of the big data mining techniques which can be used to analyze data in organizations with flooding of data. It will also show how information governance support big data. The paper begins with an overview of data mining, narrows down to the big data mining techniques and then finally the ways in which Information governance support big data.
Introduction
Data mining is the way toward looking at tremendous amounts of information so as to make a factually likely expectation. Data mining can be utilized, for example, to recognize when high going through clients connect with your business, to figure out which advancements succeed, or investigate the effect of the climate on your business. Information mining standards have been around for a long time related to information distribution centers, and have now taken on more noteworthy pervasiveness with the appearance of Enormous Information. Information examination and the development in both organized and unstructured information has likewise incited information mining strategies to change, since organizations are currently managing bigger informational collections with progressively fluctuated substance (Khan, Anjum, Soomro and Tahir, 2015). Also, man-made brainpower and AI are mechanizing the procedure of data mining.
Despite the methods applied, data mining involves three steps. These steps include exploration, modelling and deployment. The data must first be prepared and sorted out to is needed and what is not needed. This helps one to do away with useless data or even duplicates and ensuring that the final data that is sampled is the only one that is crucial and needed the most. Creating the statistical models with the aim of determining the one which will give the best and most accurate forecasting. This however can consume a lot of time as there are various and different models to the same data set which is applied severally to the sets of data respectively and finally analysis of data should be done. Lastly, in the last step, the model has to be tested against the old and the current data (Milani & Navimipour, 2017). This helps an individual to determine the results which he or she should expect in future.
Big data mining techniques
Data mining is a very significant and effective method when proper techniques are ap ...
what is ..how to process types and methods involved in data analysisData analysis ireland
Data analysis is the process of cleaning, transforming, and processing raw data in order to extract useful and actionable information that can assist businesses in making better decisions.
In our increasingly Data-driven world, it's more important than ever to have accessible ways to view and understand data.
After all, employees' demand for data skills steadily increases each year.
Employees and Business owners at every level need to understand data and its impact.
That's where Data Visualization comes in handy.
To make Data more accessible and understandable, Data Visualization in Dashboards is the go-to tool for many businesses to Analyze and share Information.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
2. Data mining
Data mining is the analysis of (often large) observational data sets to find unsuspected
relationships and to summarize the data in novel ways that are both understandable and useful to
the data owner.
Data mining Techniques
In additiontousinga particulardata miningtool, internal auditors can choose from a variety of
data miningtechniques.The mostcommonlyusedtechniquesincludeartificial neuralnetworks,decision
trees, and the nearest-neighbor method. Each of these techniques analyzes data in different ways:
Artificial neural networks are non-linear, predictive models that learn through training.
Although they are powerful predictive modeling techniques, some of the power comes at
the expense of ease of use and deployment.
One area where auditors can easily use them is when reviewing records to
identify fraud and fraud-like actions. Because of their complexity, they are better
employed in situations where they can be used and reused, such as reviewing credit card
transactions every month to check for anomalies.
Decision trees are tree-shaped structures that represent decision sets. These decisions
generate rules, which then are used to classify data. Decision trees are the favored
technique for building understandable models. Auditors can use them to assess, for
example, whether the organization is using an appropriate cost-effective marketing
strategy that is based on the assigned value of the customer, such as profit.
The nearest-neighbor method classifies dataset records based on similar data in a
historical dataset. Auditors can use this approach to define a document that is interesting
to them and ask the system to search for similar items.
3. The most commonly used techniques include artificial neural networks, decision trees,
and the nearest-neighbor method. Each of these techniques analyzes data in different ways:
Association
Association (or relation) is probably the better known and most familiar and
straightforward data mining technique. Here, it make a simple correlation between two or
more items, often of the same type to identify patterns. For example, when tracking
people's buying habits, you might identify that a customer always buys cream when they
buy strawberries, and therefore suggest that the next time that they buy strawberries they
might also want to buy cream.
Classification
It use to build up an idea of the type of customer, item, or object by describing
multiple attributes to identify a particular class. For example, you can easily classify cars
into different types (sedan, 4x4, convertible) by identifying different attributes (number
of seats, car shape, driven wheels). Given a new car, you might apply it into a particular
class by comparing the attributes with our known definition. You can apply the same
principles to customers, for example by classifying them by age and social group.
Clustering
Clustering is the task of segmenting a diverse group into a number of similar sub groups
or clusters.The distingiush clustering from classification is that clustering is not rely
on predefined classes.
The records are grouped together on the basis of self similarity.clustering is often done as
a prelude to some other form of datamining.
Prediction
4. Prediction is a wide topic and runs from predicting the failure of components or
machinery, to identifying fraud and even the prediction of company profits. Used in combination
with the other data mining techniques, prediction involves analyzing trends, classification,
pattern matching, and relation. By analyzing past events or instances, you can make a prediction
about an event.
Using the credit card authorization, for example, you might combine decision tree analysis of
individual past transactions with classification and historical pattern matches to identify whether
a transaction is fraudulent. Making a match between the purchase of flights to the US and
transactions in the US, it is likely that the transaction is valid.
Sequential patterns
Oftern used over longer-term data, sequential patterns are a useful method for identifying
trends, or regular occurrences of similar events. For example, with customer data you can
identify that customers buy a particular collection of products together at different times of the
year. In a shopping basket application, you can use this information to automatically suggest that
certain items be added to a basket based on their frequency and past purchasing history.
Decision trees
Related to most of the other techniques (primarily classification and prediction), the
decision tree can be used either as a part of the selection criteria, or to support the use and
selection of specific data within the overall structure. Within the decision tree, you start with a
simple question that has two (or sometimes more) answers. Each answer leads to a further
question to help classify or identify the data so that it can be categorized, or so that a prediction
can be made based on each answer.
Each of these approaches brings different advantages and disadvantages that need to be
considered prior to their use. Neural networks, which are difficult to implement, require all input
and resultant output to be expressed numerically, thus needing some sort of interpretation
depending on the nature of the data-mining exercise
5. The decisiontree techniqueisthe mostcommonlyused methodology, because it is simple and
straightforwardtoimplement.Finally,the nearest-neighbormethodreliesmore onlinkingsimilar items
and, therefore, works better for extrapolation rather than predictive enquiries.
A goodway to applyadvanceddataminingtechniquesis to have a flexible and interactive data
mining tool that is fully integrated with a database or data warehouse. Using a tool that operates
outside of the database or data warehouse is not as efficient
Regardlessof the technique used,the real value behind data mining is modeling — the process
of buildingamodel basedonuser-specifiedcriteriafromalreadycaptureddata.Once a model is built, it
can be used in similar situations where an answer is not known.
For example,an organization looking to acquire new customers can create a model of its ideal
customer that is based on existing data captured from people who previously purchased the product.
The model then is used to query data on prospective customers to see if they match the profile.
Modeling also can be used in audit departments to predict the number of auditors required to
undertake an audit plan based on previous attempts and similar work.