Algorithm – Tree Concept

Algorithm – Tree Concept

A binary tree is a tree data structure in which each node has at most two children. Typically the first node is known as the parent and the child nodes are called left and right.
Complete Binary tree
A complete binary tree is a binary tree in which every level, except possibly the last, is completely filled, and all nodes are as far left as possible.
Complete Binary tree is implemented using an array.
A binary search tree (BST) is a node based binary tree data structure which has the following properties:
* The left subtree of a node contains only nodes with keys less than the node's key.
* The right subtree of a node contains only nodes with keys greater than the node's key.
* Both the left and right subtrees must also be binary search trees.
Implementation
A self-balancing (or height-balanced) binary search tree is any binary search tree data structure that automatically keeps its height (number of levels below the root) small in the face of arbitrary item insertions and deletions.
Most operations on a binary search tree (BST) take time directly proportional to the height of the tree, so it is desirable to keep the height small.
In AVL tree is a self-balancing binary search tree.
In an AVL tree, the heights of the two child subtrees of any node differ by at most one; therefore, it is also said to be height-balanced. Lookup, insertion, and deletion all take O(log n) time in both the average and worst cases. Insertions and deletions may require the tree to be rebalanced by one or more tree rotations.
AVL trees perform better than red-black trees for lookup-intensive applications.
It is complex, but has good worst-case running time for its operations and is efficient in practice: it can search, insert, and delete in O(log n) time.
A red-black tree is a binary search tree where each node has a color attribute, the value of which is either red or black. In addition to the ordinary requirements imposed on binary search trees, the following additional requirements apply to red-black trees:
1. A node is either red or black.
2. The root is black. (This rule is used in some definitions and not others. Since the root can always be changed from red to black but not necessarily vice-versa this rule has little effect on analysis.)
3. All leaves are black.
4. Both children of every red node are black.
5. Every simple path from a given node to any of its descendant leaves contains the same number of black nodes.
A splay tree is a self-balancing binary search tree with the additional property that recently accessed elements are quick to access again. It performs basic operations such as insertion, look-up and removal in O(log(n)) amortized time.
All normal operations on a binary search tree are combined with one basic operation, called splaying. Splaying the tree for a certain element rearranges the tree so that the element is placed at the root of the tree. One way to do this is to first perform a standard binary tree search for the element in question, and then use tree rotations in a specific fashion to bring the element to the top. Alternatively, a top-down algorithm can combine the search and the tree reorganization into a single phase.
Advantages and disadvantages
Good performance for a splay tree depends on the fact that it is self-balancing, and indeed self optimizing, in that frequently accessed nodes will move nearer to the root where they can be accessed more quickly. This is an advantage for nearly all practical applications, and is particularly useful for implementing caches and garbage collection algorithms;
Splay trees also have the advantage of being considerably simpler to implement than other self-balancing binary search trees, such as red-black trees or AVL trees, while their average-case performance is just as efficient. Also, splay trees do not need to store any bookkeeping data, thus minimizing memory requirements. However, these other data structures provide worst-case time guarantees, and can be more efficient in practice for uniform access.
The B-tree is a generalization of a binary search tree in that more than two paths diverge from a single node.
Unlike self-balancing binary search trees, the B-tree is optimized for systems that read and write large blocks of data. It is most commonly used in databases and filesystems.
In B-trees, internal (non-leaf) nodes can have a variable number of child nodes within some pre-defined range. When data is inserted or removed from a node, its number of child nodes changes. In order to maintain the pre-defined range, internal nodes may be joined or split. Because a range of child nodes is permitted, B-trees do not need re-balancing as frequently as other self-balancing search trees, but may waste some space, since nodes are not entirely full. The lower and upper bounds on the number of child nodes are typically fixed for a particular implementation. For example, in a 2-3 B-tree (often simply referred to as a 2-3 tree), each internal node may have only 2 or 3 child nodes.
In the narrow sense, a B-tree stores keys in its internal nodes but need not store those keys in the records at the leaves.
B+ tree is a dynamic, multilevel index, with maximum and minimum bounds on the number of keys in each index segment (usually called a "block" or "node"). In a B+ tree, in contrast to a B-tree, all records are stored at the leaf level of the tree; only keys are stored in interior nodes.
A binary heap is a heap data structure created using a binary tree. It can be seen as a binary tree with two additional constraints:
* The shape property: the tree is an almost complete binary tree; that is, all levels of the tree, except possibly the last one (deepest) are fully filled, and, if the last level of the tree is not complete, the nodes of that level are filled from left to right.
* The heap property: each node is greater than or equal to each of its children according to some comparison predicate which is fixed for the entire data structure.
There are two kinds of binary heaps: max-heaps and min-heaps.
Adding to the heap
If we have a heap, and we add an element, we can perform an operation known as up-heap, bubble-up, or percolate-up in order to restore the heap property. We can do this in O(logn) time, using a binary heap, by following this algorithm:
1. Add the element on the bottom level of the heap.
2. Compare the added element with its parent; if they are in the correct order, stop.
3. if not, swap the element with its parent and return to the previous step.
Binary heap is implemented using an array.
Implementation

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