WEKA:Data Mining Input Concepts Instances And Attributes
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This document discusses concepts related to data mining input, including concepts, instances, and attributes. It also covers different types of learning in data mining like classification, numeric prediction, clustering, and association rules. Key steps for data preparation are discussed, such as data assembly and aggregation, data integration, data cleaning, and general preparation. Formats for data like the Attribute-Relation File Format (ARFF) are also introduced.
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The document provides an overview of the syllabus for a Data Structures course. It discusses topics that will be covered including arrays, linked lists, stacks, queues, trees, and graphs. It also outlines the course grading breakdown and covers basic terminology related to data structures such as data, data items, records, and files. Common data structure operations like traversing, searching, inserting, and deleting are also defined. Lastly, it provides guidance on selecting appropriate data structures based on the problem constraints and required operations.
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1- Introduction.pptx.pdf
The document introduces data structures and algorithms. It discusses that data structures include arrays, linked lists, stacks, queues, trees and graphs which can be used to organize data efficiently. It also explains that algorithms use these data structures to solve problems. The choice of data structure impacts an algorithm's time and space efficiency. Common operations on data structures like traversal, search, insertion and deletion are also introduced. Examples of different data structures and algorithms like linear search, binary search and hashing are provided to illustrate time-space tradeoffs.
introduction of database in DBMS
purpose of database systems, components of dbms, applications of
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entity relationship model, relational model
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The document provides an overview of the syllabus and topics covered in a data structures course, including data structure types, operations, and selecting appropriate data structures. It discusses linear data structures like arrays and linked lists, non-linear structures like trees and graphs, and operations like traversing, searching, inserting, and deleting. The goals of the course are to prepare students for advanced courses and teach implementing operations on different data structures using algorithms.
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Data structures provide an efficient way to store and organize data in a computer so that it can be used efficiently. They allow programmers to handle data in an enhanced way, improving software performance. There are linear data structures like arrays and linked lists, and non-linear structures like trees and graphs. Common operations on data structures include insertion, deletion, searching, sorting, and merging. Asymptotic analysis is used to define the time complexity of algorithms in the average, best, and worst cases.
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Bc0041
A foreign key is a column that references the primary key of another table. It must match a value in the referenced primary key. Unlike primary keys, foreign keys can contain null values. Primary keys uniquely identify rows in a table, while foreign keys reference primary keys to link data between tables.
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The document discusses different data structures and linear data structures specifically. It defines linear data structures as those where elements are arranged sequentially. Key linear data structures mentioned include arrays, linked lists, stacks, and queues. Linked lists are described as dynamic structures where each node contains a pointer to the next node, allowing flexible insertion and deletion. The advantages of linked lists over arrays are also summarized.
Dsa unit 1
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Modeling System Requirements
It's all about the modeling system requirements, how we can gather the requirements for modeling a system.
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The document discusses linear data structures and lists. It introduces the list abstract data type (ADT) and describes common list operations like finding an element or inserting and deleting elements. It also describes different types of lists, including singly linked lists, circularly linked lists, and doubly linked lists. The document then discusses stack and queue ADTs and their applications.
Database system
Entity relationship model, Components of ER model, Mapping E-R model to Relational schema, Network and Object-Oriented Data models, Storage Strategies: Detailed Storage Architecture, Storing Data, Magnetic Disk, RAID, Other Disks, Magnetic Tape, Storage Access, File & Record Organization, File Organizations & Indexes, Order Indices, B+ Tree Index Files, Hashing Data Dictionary
Data resource management
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WEKA:Data Mining Input Concepts Instances And Attributes
- 1. Data Mining Input: Concepts, Instances, and Attributes
- 3. Intelligible in that it can be understood
- 4. Operational in that it can be applied to actual examples
- 5. Instances: The data present consists of various instances of the class. E.g. the table below consists of 2 instances
- 7. Learning scheme is presented with a set of classified examples from which it is expected to learn a way of classifying unseen examples
- 8. Also called supervised learning
- 9. E.g. Classification rules for the weather forecasting problem If outlook = sunny and humidity = high then play = no If outlook = rainy and windy = true then play = no If outlook = overcast then play = yes
- 11. Same as classification learning but the outcome to be predicted is not a discreet class but a numeric quantity
- 12. Clustering
- 13. Groups of examples that belong together are sought and clubbed together in a cluster
- 15. Any association among features is sought, not just ones that predict a particular class value
- 16. It predicts any attribute, not just the class
- 17. It can predict more than one attribute value at a time
- 19. Flat file: Each dataset is represented as a matrix of instances versus attributes, which in database terms is a single relationship, or a flat file
- 22. Independence can be achieved by de-normalization
- 23. In database terms, take two relations and join them together to make one, a process of flattening that is technically called de-normalization
- 25. We are trying to find ‘Sister of’ relation ship Each row of tree mapped to instances: We cant make sense of this with respect to our requirement or concept. Therefore …….
- 26. We de-normalize these tables to get: Here we can clearly see the ‘Sister of’ relationship
- 27. Problems with de-normalization: If relationship between large number of items is required then tables will be huge It produces irregularities in data that are completely spurious Relations might not be finite (use: Inductive logic programming) Overlay data: Sometimes data relevant to the problem at hand needs to be collected from outside of the organization. This is called overlay data.
- 28. Data Integration Integration of system wide databases is difficult because different departments will use/have: Different style of record keeping Different conventions Different degrees of data aggregations etc Different types of errors Different time period Different primary keys These issues are taken care by the idea of company wide databases, a process called as data warehousing
- 29. Data Cleaning Data cleaning is the careful checking of data It helps in resolving many architectural issues with different databases Data cleaning usually requires good domain knowledge
- 30. Attribute-Relation File Format (ARFF) Definition: An ARFF file is an ASCII text file that describes a list of instances sharing a set of attributes Conventions used in ARFF : ARFF Header Line beginning with % are comments To declare relation: @relation <name of relation> To declare attribute: @attribute <attribute> <data type> ARFF Data Section To start the actual data: @data, followed by row wise CS data
- 31. Data type for ARFF: Numeric can be real or integer numbers Nominal values are defined by providing <nominal-specification> listing the possible values: {nm-value1, nm-value2,…} e.g. {yes, no} Values separated by space must be quoted String attributes allow us to create attributes containing arbitrary textual values Date type is used as: @attribute <name> date [<date-format>] The default date format is ISO-8601 combined date and time format:”yyyy-MM-dd’T’HH:mm:ss” Missing values are represented by ?
- 32. Sparse ARFF files Sparse ARFF files are very similar to ARFF files, but data with value 0 are not be explicitly represented Same header as ARFF but different data section. Instead of representing each value in order, like this: @data 0, X, 0, Y, “class A” The non zero attributes are explicitly identified by attribute number(starting from zero) and their value stated , like this: @data {1X, 3Y,4 “class A”}
- 33. Visit more self help tutorials Pick a tutorial of your choice and browse through it at your own pace. The tutorials section is free, self-guiding and will not involve any additional support. Visit us at www.dataminingtools.net