CS2006Ch04A
... Fast, easy to perform search but Difficult to insert and delete items Must specify size at construction time ...
... Fast, easy to perform search but Difficult to insert and delete items Must specify size at construction time ...
class8
... Data entries typically much smaller than data records. So, better than Alternative 1 with large data records, especially if search keys are small. (Portion of index structure used to direct search is much smaller than with Alternative 1.) If more than one index is required on a given file, at most o ...
... Data entries typically much smaller than data records. So, better than Alternative 1 with large data records, especially if search keys are small. (Portion of index structure used to direct search is much smaller than with Alternative 1.) If more than one index is required on a given file, at most o ...
Data Structures Lecture 1
... Queue as a Linked List We can implement a queue with a singly linked list The front element is stored at the first node The rear element is stored at the last node The space used is O(n) and each operation of the Queue ADT takes O(1) time r ...
... Queue as a Linked List We can implement a queue with a singly linked list The front element is stored at the first node The rear element is stored at the last node The space used is O(n) and each operation of the Queue ADT takes O(1) time r ...
On (Dynamic) Range Minimum Queries in External Memory
... be built in O(logm (N/M )) rounds by scanning each level of O(N ) elements for a total of O(sort(N )) I/O complexity. We process the queries in rounds, in each round processing all queries on a single level (starting with the root level) and propagating them down to the next level. For each query at ...
... be built in O(logm (N/M )) rounds by scanning each level of O(N ) elements for a total of O(sort(N )) I/O complexity. We process the queries in rounds, in each round processing all queries on a single level (starting with the root level) and propagating them down to the next level. For each query at ...
lecture22
... For each node a which is adjacent to n a’s cost = min(a’s old cost, n’s cost + cost of (n, a)) ...
... For each node a which is adjacent to n a’s cost = min(a’s old cost, n’s cost + cost of (n, a)) ...
A Case Based Study on Decision Tree Induction with AVLTree
... Generalization is the way of processing the raw data of any database may contain many attributes to be reduced by considering only the required attributes and continuing the process [16]. Attribute-Oriented induction is generalizing the data in to the required format that can replace primitive data ...
... Generalization is the way of processing the raw data of any database may contain many attributes to be reduced by considering only the required attributes and continuing the process [16]. Attribute-Oriented induction is generalizing the data in to the required format that can replace primitive data ...
Notes (part 1)
... 1. Select the entering arc. Key data structure: maintain the simplex multipliers O(1) step to determine if (i,j) is violating. 2. Determine the basic cycle C. Determine the flow around the basic cycle. Send the flow. 3. Determine the subtree T2 obtained upon deleting the exiting arc from the current ...
... 1. Select the entering arc. Key data structure: maintain the simplex multipliers O(1) step to determine if (i,j) is violating. 2. Determine the basic cycle C. Determine the flow around the basic cycle. Send the flow. 3. Determine the subtree T2 obtained upon deleting the exiting arc from the current ...
lecture18
... Prim’s Algorithm Implementation • Assume adjacency list representation Initialize connection cost of each node to “inf” and “unmark” them Choose one node, say v and set cost[v] = 0 and prev[v] =0 While they are unmarked nodes Select the unmarked node u with minimum cost; mark it For each unmarked n ...
... Prim’s Algorithm Implementation • Assume adjacency list representation Initialize connection cost of each node to “inf” and “unmark” them Choose one node, say v and set cost[v] = 0 and prev[v] =0 While they are unmarked nodes Select the unmarked node u with minimum cost; mark it For each unmarked n ...
Chapter 7 Data Structures for Strings
... point, every leaf in the subtree that the search ended at corresponds to a string that starts with s. Since every internal node has at least two children, this subtree can be traversed in O(k) time, where k is the number of leaves in the subtree. If we are only interested in reporting one string tha ...
... point, every leaf in the subtree that the search ended at corresponds to a string that starts with s. Since every internal node has at least two children, this subtree can be traversed in O(k) time, where k is the number of leaves in the subtree. If we are only interested in reporting one string tha ...
Fast Compressed Tries through Path Decompositions
... We define a binary tree as a tree where each node is either an internal node that has exactly two children or a leaf . It follows immediately that a binary tree with n internal nodes has n + 1 leaves. An example of binary trees is given by binary compacted tries. Note that there is another popular d ...
... We define a binary tree as a tree where each node is either an internal node that has exactly two children or a leaf . It follows immediately that a binary tree with n internal nodes has n + 1 leaves. An example of binary trees is given by binary compacted tries. Note that there is another popular d ...
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.