Lecture #6: Data Structure

What is Data? • Collection of data objects and their attributes • An attribute is a property or characteristic of an object – Examples: eye color of a person, temperature, etc. – Attribute is also known as variable, field, characteristic, or feature • A collection of attributes describe an object. – Object is also known as record, point, case, sample, entity, or instance 2
What is Information? • Collection of organization data or preparing data to take the decision , where Data + Processing = Information – Example: book is data while my book is information.
What is Algorithm? • Collection of steps require to solve problem and it must have the following conditions: 1. "Keep it simple, Stupid"- Don t use fancy features when simple ones suffice. 2. "Read the fascinating manual"- Most complaints from the compiler can be solved by reading the book. Logical error something else. 3. "Make your documentation short but sweet"- Always document your variable declarations, and tell what each subprogram does. 4. "Every subprogram should do something and hide something"- If you cannot concisely explain what your subprogram does, it shouldn t exist. This is why I write the header comments before I write the subroutine. 5. "Program defensively"- Add the debugging statements and routines at the begging, because you know you are going to need then later. 6. "Good program is a pretty program"- Remember that you will spend more time reading your programs than we will perfect shuffles. What is Data Structure? Data structure vs. Data Type
Where: Data type is a well-defined collection of data with a well-defined set of operations on it. Data structure is an actual implementation of a particular abstract data type, or It is study collection methods between programmer views of data and information relation by devises and it includes methods of information organization, efficient algorithms in access of information and deal with such as( insert, delete, update, sort, search. …, ect ). Data structures consist of many types as explain in the following paragraph. 3
Fig,(1): Block diagram of Data Structures What are important factors in select the suitable data structure? • Data size • Speed and use data methods • Natural dynamic of data such as exchangeable of data • Storage space require • Time require to retrieve any inform from data structure • Programming manner 4
Linear Data Structures
A. Non Linked List 1. Array 2. Stack 3. Queue B. Linked List 1. Pointer 2. Circularly Linked 3. Double Linked
Non Linear Data Structures
A. Graphics B. Trees 1. Binary Tree 2. Balanced Tree 3. Traversing Tree
Data Mining
A. What is Data Mining? B. Stages of Data Mining C. Techniques of Data Mining 1. Classification/Clustering 2. Association Rules 3. Decision Trees 4. Sequential Pattern Discovery 5. Regression 6. Deviation Detection D. Application of Association Rule Algorithms 1. PCY Algorithm 2. A Prior Algorithm
Sorting
A. Internal Sort 1. Bubble Sort 5
2. Shall Sort 3. Quick Sort 4. Address compute Sort 5. Insertion Sort 6. Radix Sort 7. Simple Merag Sort B. External Sort
Searching
A. Sequential Search B. Binary Search C. Block Search D. Tabu Search Algorithm