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Scapegoat Trees
Scapegoat Trees

... the number of keys stored in this sub-tree including the key stored at z). brother(z) - the brother of node z; the other child of z’s parent or NIL. h(z) and h(T) - height of a node and a tree respectively. The height of a node is the length of the longest path from that node to a leaf. The height o ...
Juzi: A Tool for Repairing Complex Data Structures
Juzi: A Tool for Repairing Complex Data Structures

... the structure as input, traverses it, checks for the structural integrity constraints, and returns whether it satisfies the constraints or not. The standard approach when an error is detected at runtime, say due to an assertion violation, is to terminate the program, debug it if possible, and re-exe ...
1. Define tree? root Trees are non-liner data structure, which is used
1. Define tree? root Trees are non-liner data structure, which is used

...  In linked list, inserting & deleting an element is easily performed by altering the pointers.  But finding an kth element is inefficient by traveling from the first element to the k th element.  It is not possible to delete a particular element in a singly linked list, given the pointer to that ...
Variables Storage, Type, Declaration.
Variables Storage, Type, Declaration.

Constructing the Suffix Tree of a Tree with a Large Alphabet
Constructing the Suffix Tree of a Tree with a Large Alphabet

Bulk-Loading the ND-Tree in Non-ordered Discrete Data Spaces, Best Paper Award, 13th International Conference, DASFAA 2008 (pp. 156-172), New Delhi, India, Hyun-Jeon Seik, Gang Qian, Qiang Zhu, Alexander R. Oswald and Sakti Pramanik.
Bulk-Loading the ND-Tree in Non-ordered Discrete Data Spaces, Best Paper Award, 13th International Conference, DASFAA 2008 (pp. 156-172), New Delhi, India, Hyun-Jeon Seik, Gang Qian, Qiang Zhu, Alexander R. Oswald and Sakti Pramanik.

03_quant_freq_pat_mining
03_quant_freq_pat_mining

... build a P-tree, a bit file is recursively partitioned into quadrants and each quadrant into sub-quadrants until the subquadrant is pure (entirely 1-bits or entirely 0-bits). A P-tree can be 1-dimensional, 2-dimensional, 3-dimensional, etc. For a 2-dimensional P-tree, its root contains the 1-bit coun ...
Comparison of Brute-Force and KD Tree Algorithm
Comparison of Brute-Force and KD Tree Algorithm

... the following way: every node in the tree is associated with one of the k dimensions, with the hyper-plane perpendicular to that dimension's axis. So, for example, if for a particular split the "x" axis is chosen, all points in the subtree with a smaller "x" value than the node will appear in the le ...
pptx - Electrical and Computer Engineering
pptx - Electrical and Computer Engineering

Optimal Static Range Reporting in One Dimension
Optimal Static Range Reporting in One Dimension

I/O-E cient Dynamic Point Location in Monotone Planar Subdivisions
I/O-E cient Dynamic Point Location in Monotone Planar Subdivisions

Review handout
Review handout

Introduction (CB chap. 1 & 2)
Introduction (CB chap. 1 & 2)

Linked List
Linked List

... In the previous program, we have created a simple linked list with three nodes. Let us traverse the created list and print the data of each node. For traversal, let us write a general purpose function printList() that prints any given list. ...
A Comparison of Dictionary Implementations
A Comparison of Dictionary Implementations

... 3. Remove Just like an insertion, a removal from a Red-Black tree requires the tree to be re-balanced. The search for the key to be removed, along with the re-balancing, takes O(log n) time. ...
Sec3
Sec3

Sec1 - METU
Sec1 - METU

Talk  - CSE, IIT Bombay
Talk - CSE, IIT Bombay

ppt - Courses
ppt - Courses

Data Structures Question Bank Short Answers Section 1
Data Structures Question Bank Short Answers Section 1

... 4. Given a preorder traversal of a binary search tree, can you rebuild the tree? Justify in either case. 5. Given an inorder traversal of a binary search tree, can you rebuild the tree? Justify in either case. 6. Given an postorder traversal of a binary search tree, can you rebuild the tree? Justify ...
Binary Trees
Binary Trees

lecture 9
lecture 9

COMP 620 Algorithm Analysis
COMP 620 Algorithm Analysis

... Begin at the root. If the key of the element to be searched = root key, then the search is successful. If the key of the element to be searched < root key, then search the left subtree. If the key of the element to be searched > root key, then search the right subtree. Inserting into a binary search ...
Lecture 19 Student Notes
Lecture 19 Student Notes

... there can be n − 1 heavy preferred edges and at the beginning the might have been none). But when a heavy edge becomes unpreferred, a light edge becomes preferred. We’ve already seen that there at most lg n such events per operation in the worst-case. So there are ≤ lg n events “heavy edge becomes u ...
R*-trees
R*-trees

... • Break the space into 4 equal quadrants: level-1 blocks • Level-i block: one of the four equal quadrants of a level-(i-1) block • Pixel: level-K blocks, image level-0 block • For a level-i block: all its pixels have the same prefix up to i-1 bits; the z-value of the block ...
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Interval tree

In computer science, an interval tree is a tree data structure to hold intervals. Specifically, it allows one to efficiently find all intervals that overlap with any given interval or point. It is often used for windowing queries, for instance, to find all roads on a computerized map inside a rectangular viewport, or to find all visible elements inside a three-dimensional scene. A similar data structure is the segment tree.The trivial solution is to visit each interval and test whether it intersects the given point or interval, which requires O(n) time, where n is the number of intervals in the collection. Since a query may return all intervals, for example if the query is a large interval intersecting all intervals in the collection, this is asymptotically optimal; however, we can do better by considering output-sensitive algorithms, where the runtime is expressed in terms of m, the number of intervals produced by the query. Interval trees have a query time of O(log n + m) and an initial creation time of O(n log n), while limiting memory consumption to O(n). After creation, interval trees may be dynamic, allowing efficient insertion and deletion of an interval in O(log n). If the endpoints of intervals are within a small integer range (e.g., in the range [1,...,O(n)]), faster data structures exist with preprocessing time O(n) and query time O(1+m) for reporting m intervals containing a given query point.
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