DATA STRUCTURE AND PROBLEM SOLVING
... Generally, the information represented by each node is a record rather than a single data element. However, for sequencing purposes, nodes are compared according to their keys rather than any part of their associated records. The major advantage of binary search trees over other data structures is t ...
... Generally, the information represented by each node is a record rather than a single data element. However, for sequencing purposes, nodes are compared according to their keys rather than any part of their associated records. The major advantage of binary search trees over other data structures is t ...
linear list Concept:
... before the last node in order to remove the last node. But we can not reach the node before the tail by following next links from the tail. The only way to access this node is to start from the head of the list and search all the way through the list. But such a sequence of link hopping operations c ...
... before the last node in order to remove the last node. But we can not reach the node before the tail by following next links from the tail. The only way to access this node is to start from the head of the list and search all the way through the list. But such a sequence of link hopping operations c ...
read it here
... are not balanced, as this rule does not guarantee a small height in all cases. The structure of a splay tree depends not only on the sequence of Inserts and Deletes, but also on executed Finds. The self-adjusting rule (splaying) puts recently accessed nodes near the root, so Finds on frequently or r ...
... are not balanced, as this rule does not guarantee a small height in all cases. The structure of a splay tree depends not only on the sequence of Inserts and Deletes, but also on executed Finds. The self-adjusting rule (splaying) puts recently accessed nodes near the root, so Finds on frequently or r ...
Chapter 19 Data Structures
... Data Structures Data structure: particular organization of data in memory • Group related items together • Organize so that convenient to program and efficient to execute ...
... Data Structures Data structure: particular organization of data in memory • Group related items together • Organize so that convenient to program and efficient to execute ...
Document
... needed to perform that operation) we don’t care about the multiplied or added constants. Therefore, we simply say the insert operation is of order of n or is O(n). ...
... needed to perform that operation) we don’t care about the multiplied or added constants. Therefore, we simply say the insert operation is of order of n or is O(n). ...
Week 5 Solutions 1.Which of the following is not correct with
... Which of the following is true about stack ADT implemented using linked list ? A. For push( ) operation, new nodes are inserted at the head of linked list. For pop( ) operation, nodes are removed from head. B. push( ) adds new node at head of list and pop( ) removes nodes from rear end of list ...
... Which of the following is true about stack ADT implemented using linked list ? A. For push( ) operation, new nodes are inserted at the head of linked list. For pop( ) operation, nodes are removed from head. B. push( ) adds new node at head of list and pop( ) removes nodes from rear end of list ...
Finger Search Trees - Department of Computer Science
... finger searches in O(log d) time and updates in O(1) time, assuming that only O(1) moveable fingers are maintained. Moving a finger d positions requires O(log d) time. This work was refined by Huddleston and Mehlhorn [28]. Tsakalidis [46] presented a solution based on AVL-trees, and Kosarajo [31] pr ...
... finger searches in O(log d) time and updates in O(1) time, assuming that only O(1) moveable fingers are maintained. Moving a finger d positions requires O(log d) time. This work was refined by Huddleston and Mehlhorn [28]. Tsakalidis [46] presented a solution based on AVL-trees, and Kosarajo [31] pr ...
collection part2
... • Examples – Collections framework • You can extend the framework creating your own implementations ...
... • Examples – Collections framework • You can extend the framework creating your own implementations ...
A Practical Introduction to Data Structures and Algorithm Analysis
... Problem: a task to be performed. • Best thought of as inputs and matching ...
... Problem: a task to be performed. • Best thought of as inputs and matching ...
Cache-Oblivious B-Trees
... Property 1 is normally implied by Property 2 as well as by a weaker property called weight balance. A tree is weight balanced if, for every node v, its left subtree (including v) and its right subtree (including v) have sizes that differ by at most a constant factor. Weight balancedness guarantees a ...
... Property 1 is normally implied by Property 2 as well as by a weaker property called weight balance. A tree is weight balanced if, for every node v, its left subtree (including v) and its right subtree (including v) have sizes that differ by at most a constant factor. Weight balancedness guarantees a ...
03_quant_freq_pat_mining
... all association rules that have support and confidence greater than the user-specified minimum support (called minsup) and minimum confidence (called minconf) respectively. In the most general form, an association rule can be viewed as being defined over attributes a relation and has the form C1C2, ...
... all association rules that have support and confidence greater than the user-specified minimum support (called minsup) and minimum confidence (called minconf) respectively. In the most general form, an association rule can be viewed as being defined over attributes a relation and has the form C1C2, ...
Priority Queue / Heap - Algorithms and Complexity
... • Initialize‐heap, is‐empty, get‐min, insert, and merge have worst‐case cost • Delete‐min has amortized cost • Decrease‐key has amortized cost • Starting with an empty heap, any sequence of operations with at most delete‐min operations has total cost (time) ...
... • Initialize‐heap, is‐empty, get‐min, insert, and merge have worst‐case cost • Delete‐min has amortized cost • Decrease‐key has amortized cost • Starting with an empty heap, any sequence of operations with at most delete‐min operations has total cost (time) ...
Skip Lifts: A Probabilistic Alternative to Red
... data structure (as the title of the paper suggests it) which guarantees, in worst case, a constant number of extra information per element and a constant number of structural changes per update. A skip lifts is a light version of the skip list where copies of elements have been removed from specific ...
... data structure (as the title of the paper suggests it) which guarantees, in worst case, a constant number of extra information per element and a constant number of structural changes per update. A skip lifts is a light version of the skip list where copies of elements have been removed from specific ...
Here
... When a BST becomes badly unbalanced, the search behavior can degenerate to that of a sorted linked list, O(N). There are a number of strategies for dealing with this problem; most involve adding some sort of restructuring to the insert/delete algorithms. That can be effective only if the restructuri ...
... When a BST becomes badly unbalanced, the search behavior can degenerate to that of a sorted linked list, O(N). There are a number of strategies for dealing with this problem; most involve adding some sort of restructuring to the insert/delete algorithms. That can be effective only if the restructuri ...
Binary search tree
In computer science, binary search trees (BST), sometimes called ordered or sorted binary trees, are a particular type of containers: data structures that store ""items"" (such as numbers, names and etc.) in memory. They allow fast lookup, addition and removal of items, and can be used to implement either dynamic sets of items, or lookup tables that allow finding an item by its key (e.g., finding the phone number of a person by name).Binary search trees keep their keys in sorted order, so that lookup and other operations can use the principle of binary search: when looking for a key in a tree (or a place to insert a new key), they traverse the tree from root to leaf, making comparisons to keys stored in the nodes of the tree and deciding, based on the comparison, to continue searching in the left or right subtrees. On average, this means that each comparison allows the operations to skip about half of the tree, so that each lookup, insertion or deletion takes time proportional to the logarithm of the number of items stored in the tree. This is much better than the linear time required to find items by key in an (unsorted) array, but slower than the corresponding operations on hash tables.They are a special case of the more general B-tree with order equal to two.