Data Mining Course Overview

فهرست عناوین اصلی در این پاورپوینت

فهرست عناوین اصلی در این پاورپوینت

● Data Mining
Course Overview
● About the course – Administrivia
● Grading
● Data Mining Overview
● What is Data Mining?
● Overview of terms
● Knowledge Discovery
● Examples of Large Datasets
● Examples of Data mining Applications
● How Data Mining is used
● The Data Mining Process
● Origins of Data Mining
● Data Mining Tasks
● Data Mining Methods
● Why Data Preprocessing?
● Why can Data be Incomplete?
● Data Cleaning
● Classification: Definition
● Classification Example
● Example of a Decision Tree
● Another Example of Decision Tree
● Classification: Application 1
● Classification: Application 2
● Clustering Definition
● Illustrating Clustering
● Clustering: Application 1
● Clustering: Application 2
● Illustrating Document Clustering
● Association Rule Discovery: Definition
● Association Rule Discovery: Application 1
● Data Compression
● Numerosity Reduction:
Reduce the volume of data
● Clustering
● Sampling

نوع زبان: انگلیسی حجم: 0.25 مگا بایت
نوع فایل: اسلاید پاورپوینت تعداد اسلایدها: 38 صفحه
سطح مطلب: نامشخص پسوند فایل: ppt
گروه موضوعی: زمان استخراج مطلب: 2019/06/15 08:38:19

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عبارات مهم استفاده شده در این مطلب

عبارات مهم استفاده شده در این مطلب

datum, ., mining, k, attribute, yes, set, single, class, transaction, marry, divorce, decision,

توجه: این مطلب در تاریخ 2019/06/15 08:38:19 به صورت خودکار از فضای وب آشکار توسط موتور جستجوی پاورپوینت جمع آوری شده است و در صورت اعلام عدم رضایت تهیه کننده ی آن، طبق قوانین سایت از روی وب گاه حذف خواهد شد. این مطلب از وب سایت زیر استخراج شده است و مسئولیت انتشار آن با منبع اصلی است.

http://www.cs.bu.edu/~gkollios/dm07/LectNotes/lect1.ppt

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عبارات پرتکرار و مهم در این اسلاید عبارتند از: datum, ., mining, k, attribute, yes, set, single, class, transaction, marry, divorce, decision,

مشاهده محتوای متنیِ این اسلاید ppt

مشاهده محتوای متنیِ این اسلاید ppt

data mining course overview about the course – administrivia instructor george kollios gkollios@cs.bu.edu mcs ۲۸۸ mon ۲ ۳ ۴ pm and tue ۱ ۲۵ ۱۱ ۵۵am home page http www.cs.bu.edu fac gkollios dm ۷ check frequently syllabus schedule assignments announcements… grading programming projects ۳ ۳۵ homework set ۳ ۱۵ midterm ۲ final ۳ data mining overview data warehouses and olap on line analytical processing. association rules mining clustering hierarchical and partition approaches classification decision trees and bayesian classifiers sequential pattern mining advanced topics graph mining privacy preserving data mining outlier detection spatial data mining what is data mining data mining is ۱ the efficient discovery of previously unknown valid potentially useful understandable patterns in large datasets ۲ 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 overview of terms data a set of facts items d usually stored in a database pattern an expression e in a language l that describes a subset of facts attribute a field in an item i in d. interestingness a function id l that maps an expression e in l into a measure space m overview of terms the data mining task for a given dataset d language of facts l interestingness function id l and threshold c find the expression e such that id l e c efficiently. knowledge discovery examples of large datasets government irs nga … large corporations walmart ۲ m transactions per day mobil ۱ tb geological databases at t ۳ m calls per day credit card companies scientific nasa eos project ۵ gb per hour environmental datasets examples of data mining applications ۱. fraud detection credit cards phone cards ۲. marketing customer targeting ۳. data warehousing walmart ۴. astronomy ۵. molecular biology how data mining is used ۱. identify the problem ۲. use data mining techniques to transform the data into information ۳. act on the information ۴. measure the results the data mining process ۱. understand the domain ۲. create a dataset select the interesting attributes data cleaning and preprocessing ۳. choose the data mining task and the specific algorithm ۴. interpret the results and possibly return to ۲ origins of data mining draws ideas from machine learning ai pattern recognition statistics and database systems must address enormity of data high dimensionality of data heterogeneous distributed nature of data ai machine learning statistics data mining database systems data mining tasks ۱. classification learning a function that maps an item into one of a set of predefined classes ۲. regression learning a function that maps an item to a real value ۳. clustering identify a set of groups of similar items data mining tasks ۴. dependencies and associations identify significant dependencies between data attributes ۵. summarization find a compact description of the dataset or a subset of the dataset data mining methods ۱. decision tree classifiers used for modeling classification ۲. association rules used to find associations between sets of attributes ۳. sequential patterns used to find temporal associations in time series ۴. hierarchical clustering used to group customers web users etc why data preprocessing data in the real world is dirty incomplete lacking attribute values lacking certain attributes of interest or containing only aggregate data noisy containing errors or outliers inconsistent containing discrepancies in codes or names no quality data no quality mining results quality decisions must be based on quality data data warehouse needs consistent integration of quality data required for both olap and data mining why can data be incomplete attributes of interest are not available e.g. customer information for sales transaction data data were not considered important at the time of transactions so they were not recorded data not recorder because of misunderstanding or malfunctions data may have been recorded and later deleted missing unknown values for some data data cleaning data cleaning tasks fill in missing values identify outliers and smooth out noisy data correct inconsistent data classification definition given a collection of records training set each record contains a set of attributes one of the attributes is the class. find a model for class attribute as a function of the values of other attributes. goal previously unseen records should be assigned a class as accurately as possible. a test set is used to determine the accuracy of the model. usually the given data set is divided into training and test sets with training set used to build the model and test set used to validate it. classification example categorical categorical continuous class training set learn classifier tid home owner marital status taxable income default ۱ yes single ۱۲۵k no ۲ no married ۱ k no ۳ no single ۷ k no ۴ yes married ۱۲ k no ۵ no divorced ۹۵k yes ۶ no married ۶ k no ۷ yes divorced ۲۲ k no ۸ no single ۸۵k yes ۹ no married ۷۵k no ۱ no single ۹ k yes ۱ home owner marital status taxable income default no single ۷۵k yes married ۵ k no married ۱۵ k yes divorced ۹ k no single ۴ k no married ۸ k ۱ example of a decision tree ho marst taxinc yes no no no yes no married single divorced ۸ k ۸ k splitting attributes training data model decision tree another example of decision tree categorical categorical continuous class marst ho taxinc yes no no yes no married single divorced ۸ k ۸ k there could be more than one tree that fits the same data tid home owner marital status taxable income default ۱ yes single ۱۲۵k no ۲ no married ۱ k no ۳ no single ۷ k no ۴ yes married ۱۲ k no ۵ no divorced ۹۵k yes ۶ no married ۶ k no ۷ yes divorced ۲۲ k no ۸ no single ۸۵k yes ۹ no married ۷۵k no ۱ no single ۹ k yes ۱ classification application ۱ direct marketing goal reduce cost of mailing by targeting a set of consumers likely to buy a new cell phone product. approach use the data for a similar product introduced before. we know which customers decided to buy and which decided otherwise. this buy don’t buy decision forms the class attribute. collect various demographic lifestyle and company interaction related information about all such customers. type of business where they stay how much they earn etc. use this information as input attributes to learn a classifier model. from berry linoff data mining techniques ۱۹۹۷ classification application ۲ fraud detection goal predict fraudulent cases in credit card transactions. approach use credit card transactions and the information on its account holder as attributes. when does a customer buy what does he buy how often he pays on time etc label past transactions as fraud or fair transactions. this forms the class attribute. learn a model for the class of the transactions. use this model to detect fraud by observing credit card transactions on an account. clustering definition given a set of data points each having a set of attributes and a similarity measure among them find clusters such that data points in one cluster are more similar to one another. data points in separate clusters are less similar to one another. similarity measures euclidean distance if attributes are continuous. other problem specific measures. illustrating clustering euclidean distance based …

کلمات کلیدی پرکاربرد در این اسلاید پاورپوینت: datum, ., mining, k, attribute, yes, set, single, class, transaction, marry, divorce, decision,

این فایل پاورپوینت شامل 38 اسلاید و به زبان انگلیسی و حجم آن 0.25 مگا بایت است. نوع قالب فایل ppt بوده که با این لینک قابل دانلود است. این مطلب برگرفته از سایت زیر است و مسئولیت انتشار آن با منبع اصلی می باشد که در تاریخ 2019/06/15 08:38:19 استخراج شده است.

http://www.cs.bu.edu/~gkollios/dm07/LectNotes/lect1.ppt

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