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Intrusion Detection based on KDD Cup Dataset
 
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Final Presentation for Big Data Analysis
Views: 8288 Qiankun Zhuang
Last Minute Tutorials | KDD | Knowledge Discovery of Data
 
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Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 16069 Last Minute Tutorials
KDD-CUP 99 problem
 
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KDD-CUP 99 PROBLEM solutions
Views: 1648 QIQI SHI
Data Mining for Network Intrusion Detection
 
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Data Mining for Network Intrusion Detection: Experience with KDDCup’99 Data set
Views: 1210 Jessy Li
KDD-CUP 99
 
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Solution foR KDD-CUP 99
Views: 2174 QIQI SHI
KDD Cup Classification
 
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Big Data Final
Views: 343 Freddie Zhang
Final Year Projects | Effective Analysis of KDD data for Intrusion Detection
 
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Final Year Projects | Effective Analysis of KDD data for Intrusion Detection More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 4869 Clickmyproject
12.2: Programming KDD99 with Keras TensorFlow, Intrusion Detection System (IDS) (Module 12, Part 2)
 
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Creating an intrusion detection system (IDS) with Keras and Tensorflow, with the KDD-99 dataset. This video is part of a course that is taught in a hybrid format at Washington University in St. Louis; however, all the information is online and you can easily follow along. T81-558: Application of Deep Learning, at Washington University in St. Louis Please subscribe and comment! Follow me: YouTube: https://www.youtube.com/user/HeatonResearch Twitter: https://twitter.com/jeffheaton GitHub: https://github.com/jeffheaton More links: Complete course: https://sites.wustl.edu/jeffheaton/t81-558/ Complete playlist: https://www.youtube.com/playlist?list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN
Views: 5011 Jeff Heaton
Data Mining   KDD Process
 
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KDD - knowledge discovery in Database. short introduction on Data cleaning,Data integration, Data selection,Data mining,pattern evaluation and knowledge representation.
An Internal Intrusion Detection and Protection System by Using Data Mining and Forensic Techniques
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 869 Clickmyproject
KDD99 - Machine Learning for Intrusion Detectors from attacking data
 
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Machine Learning for Intrusion Detectors from attacking data
Views: 4340 Chao-Hung Chen
CloudVista Demo: visualizing the extended KDD Cup 1999 Dataset
 
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CloudVista: visualizing the clustering structure in the "extended" KDD Cup 1999 training data, which has about 40 million records, 41 dimensions, 13.5GB in total. The original dataset has about 4 million records. The additional records in the extended version are generated by randomly picking one original record and adding a random noise to each dimension. The extended version is used to show how larger datasets can be possibly visualized in the CloudVista system. Frame rate: 4 frames per second The original dataset can be found at http://archive.ics.uci.edu/ml/datasets/KDD+Cup+1999+Data
Views: 881 gtkeke
Concept Drift Detector in Data Stream Mining
 
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Jorge Casillas, Shuo Wang, Xin Yao, Concept Drift Detection in Histogram-Based Straightforward Data Stream Classification, 6th International Workshop on Data Science and Big Data Analytics, IEEE International Conference on Data Mining, November 17-20, 2018 - Singapore http://decsai.ugr.es/~casillas/downloads/papers/casillas-ci44-icdm18.pdf This presentation shows a novel algorithm to accurately detect changes in non-stationary data streams in a very efficiently way. If you want to know how the yacare caiman, the cheetah and the racer snake are related to this research, do not stop watching the video! More videos here: http://decsai.ugr.es/~casillas/videos.html
Views: 128 Jorge Casillas
Feature Selection in the Corrected KDD -dataset - Dr Zargari
 
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Dr Shahrzad Zargari, Senior Lecturer in Information and System Security, Sheffield Hallam University, Feature Selection in the Corrected KDD -dataset
Views: 31 The Cyber Academy
Testing and Training of Data Set Using Weka
 
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how to train and test data in weka data mining using csv file
Views: 14581 Tutorial Spot
Analysis of Intrusion Detection from KDD Cup 99 Dataset both Labelled and Unlabelled
 
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Title: Analysis of Intrusion Detection from KDD Cup 99 Dataset both Labelled and Unlabelled Domain: Data Mining Description: Intrusion Detection is one of the high priorities & the challenging tasks for network administrators & security experts. Intrusion detection system is employed to protect the data integrity, confidentiality and system availability from attacks. Data mining has been used extensively and broadly by several network organizations. IDS use the data mining techniques to analyze the resources from the database over a network. It is also necessary to develop a robust algorithm to generate effective rules for detecting the attacks. In this paper a flexible architectural system is proposed that uses Associative Classification (AC) method called Multi-label Classifier based Associative Classification (MCAC) to get better results in terms of accuracy, false alarm rate, efficiency, capability to detect new type of attacks. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in cloud computing 4. 2017 – 2018 cloud computing projects 5. 2017 – 2018 cloud simulation projects 6. 2017 – 2018 cloud sim projects 7. 2017 – 2018 best project center in Chennai 8. Ieee cloud simulation projects in Chennai 9. 2017 – 2018 real time cloud hosting 10. 2017 – 2018 aws console ieee projects 11. aws console real time ieee projects 12. ieee projects deployment in aws console 13. cloud computing projects in amazon cloud server 14. ieee projects in Amazon cloud server 15. amazon cloud server ieee projects 16. 2017 ieee real time cloud projects 17. Cloud sim projects in cloud computing 18. ieee projects in green computing 19. ieee projects in cloud computing 20. green computing ieee projects 21. ieee projects in big data 22. ieee projects in hadoop 23. ieee projects in mango db 24. mango db ieee projects 25. hadoop projects in cloud computing 26. cloud sim projects for final year b.e students 27. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 28. 2017 – 2018 b.e projects 29. 2017 – 2018 m.e projects 30. 2017 – 2018 final year projects 31. Affordable final year projects 32. Latest final year projects 33. Best project center in Chennai, Coimbatore, Bangalore, and Mysore 34. 2017 Best ieee project titles 35. Best projects in java domain 36. Free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 37. 2017 – 2018 ieee base paper free download 38. 2017 – 2018 ieee titles free download 39. best ieee projects in affordable cost 40. ieee projects free download 41. 2017 cloud sim projects 42. 2017 ieee projects on cloud sim 43. 2017 final year cloud sim projects 44. 2017 cloud sim projects for b.e 45. 2017 cloud sim projects for m.e 46. 2017 latest cloud sim projects 47. latest cloud sim projects 48. latest cloud sim projects in java 49. cloud computing cloud sim projects 50. green computing projects in cloud sim 51. mini projects on big data 52. 2017 mini projects on big data 53. final year project on big data 54. big data topics for project 55. ieee projects based on big data 56. big data project ieee ideas for students 57. big data projects for engineering students 58. 2017 ieee big data projects 59. ieee projects on big data 2017 60. cloud computing projects java source code 61. cloud computing projects with source code 62. 2017 mini project on cloud computing 63. cloud computing mini projects 64. mini projects in java 65. ieee projects on cloud computing 66. projects based on cloud computing 67. best cloud computing projects 68. cloud computing project topics 69. major projects on cloud computing 70. final year projects for cse 71. ieee cloud computing projects for cse 72. cloud computing mini project 73. cloud computing related projects 74. cloud computing based projects for final year 75. cloud computing based project ideas 76. cloud computing application projects 77. cloud computing project topics in java 78. cloud computing projects for engineering students
Views: 2383 InnovationAdsOfIndia
KDD99 Data Set Analysis
 
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Analyze KDD99 data set by Sean Han
Views: 3578 Xiao Han
Enter a KDD Cup or Kaggle Competition. You Don't Need to Be an Expert!
 
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In this on-demand webinar, we will show you how TreeNet Gradient Boosting can be used for the 2009 KDD Cup competition to quickly achieve a place in the top 5. At the end of this webinar, our goal is that you will be able to build a TreeNet model that can bring you within decimal places of a winning solution. Use this information as a starting point for Kaggle competitions and other KDD Cup competitions. The software, datasets, and a step-by-step tutorial on how to recreate the results shown can be accessed here: http://info.salford-systems.com/kaggle-competition-kddcup-0 http://www.salford-systems.com
Views: 956 Salford Systems
kddcup experiment
 
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Lubron
Views: 11 Lubron Zhan
Neo Metrics, premiada en la KDD CUP 2007
 
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.- La empresa española Neo Metrics ha resultado premiada en la décima edición de la KDD CUP 2007, la más importante y prestigiosa competición anual de data mining y análisis de bases de datos.
Views: 1319 IDGtv
Application of Machine Learning Techniques for Network Intrusion Detection System - DEMO
 
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Due to the increasing number of attacks in cyberspace. Network intrusion detection becomes a more difficult task. Many Network Intrusion Detection System uses data mining and machine learning techniques. Most researchers use dataset KDD Cup 99, which have been widely criticized for not being able to display the current network situation. In this project, we use a new dataset of networked called the Kyoto 2006+. In this dataset. All data is labeled as normal connections, attack connections and unknown attack. This project compares the prediction results from three algorithms, Decision Tree, Neural Network and K-Nearest Neighbor. In summary will uses the Decision Tree (J48) algorithm to group network connections. This can be used with network intrusion detection systems. For Model training and Model testing. This project uses 269,330 network connection samples. The generated rule works with 98.0878% accuracy. Then, the generated rule implements a program that predicts input data from file and displays the confusion matrix, which uses C language for coding.
Views: 174 NUTTHAPON CHUAYKERT
2000-10-11 CERIAS - Developing Data Mining Techniques for Intrusion Detection: A Progress Report
 
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Recorded: 10/11/2000 CERIAS Security Seminar at Purdue University Developing Data Mining Techniques for Intrusion Detection: A Progress Report Wenke Lee, North Carolina State University Intrusion detection (ID) is an important component of infrastructure protection mechanisms. Intrusion detection systems (IDSs) need to be accurate, adaptive, extensible, and cost-effective. These requirements are very challenging because of the complexities of today's network environments and the lack of IDS development tools. Our research aims to systematically improve the development process of IDSs. In the first half of the talk, I will describe our data mining framework for constructing ID models. This framework mines activity patterns from system audit data and extracts predictive features from the patterns. It then applies machine learning algorithms to the audit records, which are processed according to the feature definitions, to generate intrusion detection rules. This framework is a "toolkit" (rather than a "replacement") for the IDS developers. I will discuss the design and implementation issues in utilizing expert domain knowledge in our framework. In the second half of the talk, I will give an overview of our current research efforts, which include: cost-sensitive analysis and modeling techniques for intrusion detection; information-theoretic approaches for anomaly detection; and correlation analysis techniques for understanding attack scenarios and early detection of intrusions. Wenke Lee is an Assistant Professor in the Computer Science Department at North Carolina State University. He received his Ph.D. in Computer Science from Columbia University and B.S. in Computer Science from Zhongshan University, China. His research interests include network security, data mining, and workflow management. He is a Principle Investigator (PI) for research projects in intrusion detection and network management, with funding from DARPA, North Carolina Network Initiatives, Aprisma Management Technologies, and HRL Laboratories. He received a Best Paper Award (applied research category) at the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99), and Honorable Mention (runner-up) for Best Paper Award (applied research category) at both KDD-98 and KDD-97. He is a member of ACM and IEEE. (Visit: www.cerias.purdue.edu)
Views: 1611 ceriaspurdue
Building an intrusion detection system using a filter-based feature selection algorithm
 
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Building an intrusion detection system using a filter-based feature selection algorithm in Java TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.
Views: 4363 jpinfotechprojects
KDD Cupset Intrusion Detection DataSet Import to MYSQL Database - Simpleway How to use KDD Cupset
 
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This tutorial tells you how to import KDD Cupset in MYSQL Database INTRUSION DETECTOR LEARNING http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html www.mysqldumper.net/‎ This is the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99 The Fifth International Conference on Knowledge Discovery and Data Mining. The competition task was to build a network intrusion detector, a predictive model capable of distinguishing between ``bad'' connections, called intrusions or attacks, and ``good'' normal connections. This database contains a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment.
A Simple Method to Predict Affiliation Ranking in KDDCup 2016
 
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Author: Ping-I Chou, Trend Micro Inc. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 48 KDD2016 video
Application of Machine Learning Techniques for Network Intrusion Detection System - FULL Version
 
11:36
Due to the increasing number of attacks in cyberspace. Network intrusion detection becomes a more difficult task. Many Network Intrusion Detection System uses data mining and machine learning techniques. Most researchers use dataset KDD Cup 99, which have been widely criticized for not being able to display the current network situation. In this project, we use a new dataset of networked called the Kyoto 2006+. In this dataset. All data is labeled as normal connections, attack connections and unknown attack. This project compares the prediction results from three algorithms, Decision Tree, Neural Network and K-Nearest Neighbor. In summary will uses the Decision Tree (J48) algorithm to group network connections. This can be used with network intrusion detection systems. For Model training and Model testing. This project uses 269,330 network connection samples. The generated rule works with 98.0878% accuracy. Then, the generated rule implements a program that predicts input data from file and displays the confusion matrix, which uses C language for coding.
How to do the Knowledge Discovery (KDD) process in WEKA using Knowledge Flow
 
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Waikato Environment for Knowledge Analysis is a suite of machine learning software written in Java, developed at the University of Waikato, New Zealand. It is free software licensed under the GNU General Public License. #RanjiRaj #WEKA #KDD Follow me on Instagram 👉 https://www.instagram.com/reng_army/ Visit my Profile 👉 https://www.linkedin.com/in/reng99/ Support my work on Patreon 👉 https://www.patreon.com/ranjiraj
Views: 3053 Ranji Raj
How to use WEKA software for data mining tasks
 
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Also i recommended my new cryptocurrencies signal programm - https://dropmefiles.com/4b15l . Now free FREE Trial. bitcoin mining cryptocurrency cryptocurrency exchange dogecoin best cryptocurrency litecoin cryptocurrency market crypto what is cryptocurrency cryptocurrency charts cryptocurrency value cryptocurrency wallet cryptocurrency news cryptocurrency market cap bitcoin price ethereum reddit cryptocurrency cryptocurrency miner crypto currency btc cryptocurrency list ripple cryptocurrency doge Altcoins Bit Satoshi XBT and BTC Confirmation Recovery phrase/seed keyword Cryptography Private Key Public Key/Bitcoin address Bitcoin wallet (Hardware, Software, Mobile wallet) Transaction ID Blockchain Cold storage HD Wallet Hardware wallet Transaction fees P2P Block Proof Of Work Pump & Dump Hash ICO Hard Fork Soft Fork Faucet Fiat Block Reward ASIC Miner Block Height Halving Hash Rate Crypto Exchange Limit Order (Limit Buy/Limit Sell) HODL Whale Bullish Bearish ATH FUD FUDster To The Moon Bag Holder
Views: 58 Sh aaazam
Data Mining using R | R Tutorial for Beginners | Data Mining Tutorial for Beginners 2018 | ExcleR
 
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Data Mining Using R (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data Mining Certification Training Course Content : https://www.excelr.com/data-mining/ Introduction to Data Mining Tutorials : https://youtu.be/uNrg8ep_sEI What is Data Mining? Big data!!! Are you demotivated when your peers are discussing about data science and recent advances in big data. Did you ever think how Flip kart and Amazon are suggesting products for their customers? Do you know how financial institutions/retailers are using big data to transform themselves in to next generation enterprises? Do you want to be part of the world class next generation organisations to change the game rules of the strategy making and to zoom your career to newer heights? Here is the power of data science in the form of Data mining concepts which are considered most powerful techniques in big data analytics. Data Mining with R unveils underlying amazing patterns, wonderful insights which go unnoticed otherwise, from the large amounts of data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, unbiased and scientific-driven decisions. Data mining has powerful tools and techniques that answer business questions in a scientific manner, which traditional methods cannot answer. Adoption of data mining concepts in decision making changed the companies, the way they operate the business and improved revenues significantly. Companies in a wide range of industries such as Information Technology, Retail, Telecommunication, Oil and Gas, Finance, Health care are already using data mining tools and techniques to take advantage of historical data and to create their future business strategies. Data mining can be broadly categorized into two branches i.e. supervised learning and unsupervised learning. Unsupervised learning deals with identifying significant facts, relationships, hidden patterns, trends and anomalies. Clustering, Principle Component Analysis, Association Rules, etc., are considered unsupervised learning. Supervised learning deals with prediction and classification of the data with machine learning algorithms. Weka is most popular tool for supervised learning. Topics You Will Learn… Unsupervised learning: Introduction to datamining Dimension reduction techniques Principal Component Analysis (PCA) Singular Value Decomposition (SVD) Association rules / Market Basket Analysis / Affinity Filtering Recommender Systems / Recommendation Engine / Collaborative Filtering Network Analytics – Degree centrality, Closeness Centrality, Betweenness Centrality, etc. Cluster Analysis Hierarchical clustering K-means clustering Supervised learning: Overview of machine learning / supervised learning Data exploration methods Basic classification algorithms Decision trees classifier Random Forest K-Nearest Neighbours Bayesian classifiers: Naïve Bayes and other discriminant classifiers Perceptron and Logistic regression Neural networks Advanced classification algorithms Bayesian Networks Support Vector machines Model validation and interpretation Multi class classification problem Bagging (Random Forest) and Boosting (Gradient Boosted Decision Trees) Regression analysis Tools You Will Learn… R: R is a programming language to carry out complex statistical computations and data visualization. R is also open source software and backed by large community all over the world who are contributing to enhancing the capability. R has many advantages over other tools available in the market and it has been rated No.1 among the data scientist community. Mode of Trainings : E-Learning Online Training ClassRoom Training --------------------------------------------------------------------------- For More Info Contact :: Toll Free (IND) : 1800 212 2120 | +91 80080 09704 Malaysia: 60 11 3799 1378 USA: 001-608-218-3798 UK: 0044 203 514 6638 AUS: 006 128 520-3240 Email: [email protected] Web: www.excelr.com
Paper Data Mining for Network Intrusion Detection
 
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كةمبيني بةكوردي كردني زانست لة زانكؤي كةشةبيَداني مرؤيي
Views: 177 Hawnaz Mustafa
KDD presentation for Sun Xing
 
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KDD presentation for Sun Xing, analyze KDD 99 data set.
Views: 119 Xiao Han
First time Weka Use : How to create & load data set in Weka : Weka Tutorial # 2
 
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This video will show you how to create and load dataset in weka tool. weather data set excel file https://eric.univ-lyon2.fr/~ricco/tanagra/fichiers/weather.xls
Views: 38756 HowTo
DataAnalytics KDD Intro 20140922
 
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Big Data - Data mining is also sometimes called Knowledge Discovery in Databases (KDD).
Views: 217 Training