3.4 selection sort
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Selection sort works by iteratively finding the minimum/maximum element from the unsorted portion of the list and placing it at the end/beginning of the sorted portion. It does this by scanning the unsorted portion to find the minimum/maximum element, swapping it with the element in the last position of the unsorted portion, and moving it to the sorted portion. This process is repeated until the entire list is sorted.
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- The largest cluster comprised 34% of customers who sometimes make unplanned purchases for others and are influenced by sales promotions. They tend to be female, aged 16-25, and have a monthly income around $1400.
- Clustering the customer data in WEKA allows the store to better understand customer segments and target specific groups with tailored marketing.
Sorting in python
This will introduce you to Sorting in Python and help you understand the importance of Sorting. This Python tutorial will take you through various Sorting Algorithms. The Algorithms are lined after one another, starting with Bubble Sort moving to Insertion Sort, Merge Sort, and finally Quick Sort. The demonstration examples will educate you on Sorting Algorithms.
Data-Driven Approach to Search Relevance
At Measured Search, we enable companies to elevate experience of search based applications faster and with more confidence. SearchStax, our platform amplifies open source search engines (Solr and Elasticsearch) to accelerate development, reduce operational overhead and improve performance. Join Measured Search's Eric Melz and Ticketmaster's Praveena Subrahmanyam to learn how Ticketmaster is moving towards a data driven search culture and creating a world class search experience for their live entertainment fans.
Searching algorithms
The document discusses different types of searching algorithms. It describes sequential search which searches an unsorted list sequentially until the target item is found or the entire list is searched. The average runtime is O(n) as the item could be anywhere in the list. Binary search is described as more efficient for sorted lists, repeatedly dividing the search space in half and comparing the target to the middle element. Indexes are also summarized, including clustered vs unclustered indexes and different approaches to storing data entries.
Searching algorithms
The document discusses different searching algorithms. It describes sequential search which compares the search key to each element in the list sequentially until a match is found. The best case is 1 comparison, average is N/2 comparisons, and worst case is N comparisons. It also describes binary search which divides the sorted list in half at each step, requiring log(N) comparisons in the average and worst cases. The document also covers indexing which structures data for efficient retrieval based on key values and includes clustered vs unclustered indexes.
366 it elective 4 (analysis of algoritm)
The document discusses insertion sort, an algorithm that builds a sorted list one item at a time by inserting each element into the correct position in a growing sorted list. It explains that insertion sort maintains a sorted sub list and inserts new items into the proper place within the sub list. Pseudocode and C code examples of insertion sort are provided.
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5.5 back tracking
The document discusses backtracking algorithms. It begins by defining backtracking as a methodical way to try different sequences of decisions to solve a problem until a solution is found. It then provides examples of backtracking for finding a maze path and coloring a map. The key aspects of a backtracking algorithm are that it uses depth-first search to recursively explore choices, pruning paths that do not lead to solutions.
5.5 back track
The document discusses various backtracking algorithms and problems. It begins with an overview of backtracking as a general algorithm design technique for problems that involve traversing decision trees and exploring partial solutions. It then provides examples of specific problems that can be solved using backtracking, including the N-Queens problem, map coloring problem, and Hamiltonian circuits problem. It also discusses common terminology and concepts in backtracking algorithms like state space trees, pruning nonpromising nodes, and backtracking when partial solutions are determined to not lead to complete solutions.
5.5 back tracking 02
The document discusses the divide and conquer algorithm design strategy. It begins by explaining the general concept of divide and conquer, which involves splitting a problem into subproblems, solving the subproblems, and combining the solutions. It then provides pseudocode for a generic divide and conquer algorithm. Finally, it gives examples of divide and conquer algorithms like quicksort, binary search, and matrix multiplication.
5.4 randomized datastructures
This document discusses randomized data structures and algorithms. It begins by motivating randomized data structures as a way to transform average case runtimes into expected runtimes that are not dependent on specific inputs. It then provides examples of randomized data structures like treaps and randomized skip lists that provide efficient operations like insertion, deletion, and search in expected logarithmic time. It also discusses how randomization can be applied in algorithms like primality testing.
5.4 randomized datastructures
This document discusses randomized data structures and algorithms. It begins by motivating randomized data structures by noting that some data structures like binary search trees have average case performance but worst case inputs. Randomizing the data structure removes dependency on inputs and provides expected case performance. The document then discusses treaps and randomized skip lists as examples of randomized data structures that provide efficient expected case performance for operations like insertion, deletion, and search. It also covers topics like randomized number generation, primality testing, and how randomization can transform average case runtimes into expected case runtimes.
5.4 randamized algorithm
Skip lists are a data structure for implementing dictionaries. They consist of multiple sorted lists, with the top list containing all elements and lower lists being subsequences. Searching works by dropping down lists until finding the target element or determining it is absent. Insertion and deletion use a randomized algorithm adding/removing elements from the appropriate lists. Analysis shows the expected space is O(n) and search, insertion and deletion times are O(log n), with these bounds also holding with high probability. Skip lists provide fast, simple dictionary implementation in practice.
5.3 dynamic programming 03
Dynamic programming is an algorithm design paradigm that can be applied to problems exhibiting optimal substructure and overlapping subproblems. It works by breaking down a problem into subproblems and storing the results of already solved subproblems, rather than recomputing them multiple times. This allows for an efficient bottom-up approach. Examples where dynamic programming can be applied include the matrix chain multiplication problem, the 0-1 knapsack problem, and finding the longest common subsequence between two strings.
5.3 dynamic programming
Dynamic programming is a technique for solving problems with overlapping subproblems and optimal substructure. It works by breaking problems down into smaller subproblems and storing the results in a table to avoid recomputing them. Examples where it can be applied include the knapsack problem, longest common subsequence, and computing Fibonacci numbers efficiently through bottom-up iteration rather than top-down recursion. The technique involves setting up recurrences relating larger instances to smaller ones, solving the smallest instances, and building up the full solution using the stored results.
5.3 dyn algo-i
This document summarizes a talk on dynamic graph algorithms. It begins with an introduction to dynamic graph algorithms, which involve maintaining a graph structure and answering queries efficiently as the graph undergoes a sequence of edge insertions and deletions. It then discusses several examples of fully dynamic algorithms for problems like connectivity, minimum spanning trees, and graph spanners. A key data structure introduced is the Euler tour tree, which represents a dynamic tree as a one-dimensional structure to support efficient updates and queries. The document concludes by outlining a fully dynamic randomized algorithm for maintaining connectivity under edge updates with polylogarithmic update time, using a hierarchical approach with multiple levels of edge partitions and ET trees.
5.2 divide and conquer
This document discusses the merge sort algorithm for sorting a sequence of numbers. It begins by introducing the divide and conquer approach, which merge sort uses. It then provides an example of how merge sort works, dividing the sequence into halves, sorting the halves recursively, and then merging the sorted halves together. The document proceeds to provide pseudocode for the merge sort and merge algorithms. It analyzes the running time of merge sort using recursion trees, determining that it runs in O(n log n) time. Finally, it covers techniques for solving recurrence relations that arise in algorithms like divide and conquer approaches.
5.2 divede and conquer 03
This document discusses divide-and-conquer algorithms and their time complexities. It begins with examples of finding the maximum of a set and binary search. It then presents the general steps of a divide-and-conquer algorithm and analyzes time complexity. Several algorithms are discussed including quicksort, merge sort, 2D maxima finding, closest pair problem, convex hull problem, and matrix multiplication. Strategies like divide, conquer, and merge are used to solve problems recursively in fewer comparisons than brute force methods. Many algorithms have a time complexity of O(n log n).
5.1 greedyyy 02
The document discusses greedy algorithms and provides examples of problems that can be solved using greedy techniques. It introduces the coin changing problem and activity selection problem. For activity selection, it demonstrates that a greedy approach of always selecting the activity with the earliest finish time results in an optimal solution. It provides pseudo-code for a greedy algorithm and proves that the greedy solution is optimal for the activity selection problem by showing there is always an optimal solution that makes the greedy choice and combining the greedy choice with the optimal solution to the remaining subproblem yields an optimal solution to the original problem.
5.1 greedy
The document discusses various optimization problems that can be solved using the greedy method. It begins by explaining that the greedy method involves making locally optimal choices at each step that combine to produce a globally optimal solution. Several examples are then provided to illustrate problems that can and cannot be solved with the greedy method. These include shortest path problems, minimum spanning trees, activity-on-edge networks, and Huffman coding. Specific greedy algorithms like Kruskal's algorithm, Prim's algorithm, and Dijkstra's algorithm are also covered. The document concludes by noting that the greedy method can only be applied to solve a small number of optimization problems.
5.1 greedy 03
This document discusses greedy algorithms and provides examples of their use. It begins by defining characteristics of greedy algorithms, such as making locally optimal choices that reduce a problem into smaller subproblems. The document then covers designing greedy algorithms, proving their optimality, and analyzing examples like the fractional knapsack problem and minimum spanning tree algorithms. Specific greedy algorithms covered in more depth include Kruskal's and Prim's minimum spanning tree algorithms and Huffman coding.
4.4 hashing02
The document outlines various data structures and algorithms for implementing dictionaries and hash tables, including:
- Separate chaining, which handles collisions by storing elements that hash to the same value in a linked list. Find, insert, and delete take average time of O(1).
- Open addressing techniques like linear probing and quadratic probing, which handle collisions by probing to alternate locations until an empty slot is found. These have faster search but slower inserts and deletes.
- Double hashing, which uses a second hash function to determine probe distances when collisions occur, reducing clustering compared to linear probing.
4.4 hashing
This document discusses hashing and hash tables. It begins by introducing hash tables and describing how hashing works by mapping keys to array indices using a hash function to allow for fast insertion, deletion and search operations in O(1) average time. However, hash tables do not support ordering of elements efficiently. The document then discusses issues with hash functions such as collisions when different keys map to the same index. It describes techniques for collision resolution including separate chaining, where each index points to a linked list, and open addressing techniques like linear probing, quadratic probing and double hashing that resolve collisions by probing alternate indices in the array.
4.4 hashing ext
This document summarizes key points about extendible hashing and discusses its use for a spelling dictionary case study. Extendible hashing is a hashing technique that optimizes disk accesses for huge datasets by storing hash buckets in disk blocks. It uses a directory to hash to the correct bucket. The document explains how to insert keys, split buckets, and rehash the table. It also discusses solutions for the spelling dictionary case study, comparing storage and time efficiency of sorted arrays, open hashing, and closed hashing with linear probing.
4.4 external hashing
This document discusses extendible hashing, which is a hashing technique for dynamic files that allows efficient insertion and deletion of records. It works by using a directory to map hash values to buckets, and dynamically expanding the directory size and number of buckets as needed to accommodate new records. When a bucket overflows, it is split into two buckets, and the directory is expanded to distinguish them. The directory size can also be contracted when buckets can be combined due to deletions. Alternative approaches like dynamic hashing and linear hashing that address the same problem of dynamic files are also overviewed.
4.2 bst
The document discusses searching data structures like binary search trees and linked lists. It provides pseudocode for iterative searching algorithms on both ordered and unordered linked lists. The algorithms traverse the list by iterating through each node until the target value is found or the end is reached. For ordered lists, searching can stop early if the current node value is greater than the target. Tracing examples are provided to demonstrate searching for a value in sample linked lists.
4.2 bst 03
1) Tree data structures involve nodes that can have zero or more child nodes and at most one parent node. Binary trees restrict nodes to having zero, one, or two children.
2) Binary search trees have the property that all left descendants of a node are less than the node's value and all right descendants are greater. This property allows efficient searches, inserts, and deletes that take O(log n) time on average.
3) Trees can become unbalanced over many insertions and deletions, affecting performance of operations. Various self-balancing binary search tree data structures use tree rotations to maintain balance.
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3.4 selection sort
- 2. Selection Sort: Idea 1. We have two group of items: sorted group, and unsorted group 1. Initially, all items are in the unsorted group. The sorted group is empty. We assume that items in the unsorted group unsorted. We have to keep items in the sorted group sorted.
- 3. Selection Sort: Cont’d 1. Select the “best” (eg. smallest) item from the unsorted group, then put the “best” item at the end of the sorted group. 2. Repeat the process until the unsorted group becomes empty.
- 4. Selection Sort Example 35 65 30 60 20 scan 0-4, smallest 20 swap 35 and 20 20 65 30 60 35 scan 1-4, smallest 30 swap 65 and 30 20 30 65 60 35 scan 2-4, smallest 35 swap 65 and 35 Chapter 10: Sorting 4
- 5. Selection Sort 5 1 3 4 6 2 Comparison Data Movement Sorted
- 6. Selection Sort 5 1 3 4 6 2 Comparison Data Movement Sorted
- 7. Selection Sort 5 1 3 4 6 2 Comparison Data Movement Sorted
- 8. Selection Sort 5 1 3 4 6 2 Comparison Data Movement Sorted
- 9. Selection Sort 5 1 3 4 6 2 Comparison Data Movement Sorted
- 10. Selection Sort 5 1 3 4 6 2 Comparison Data Movement Sorted
- 11. Selection Sort 5 1 3 4 6 2 Comparison Data Movement Sorted
- 12. Selection Sort 5 1 3 4 6 2 Comparison Data Movement Sorted Largest
- 13. Selection Sort 5 1 3 4 2 6 Comparison Data Movement Sorted
- 14. Selection Sort 5 1 3 4 2 6 Comparison Data Movement Sorted
- 15. Selection Sort 5 1 3 4 2 6 Comparison Data Movement Sorted
- 16. Selection Sort 5 1 3 4 2 6 Comparison Data Movement Sorted
- 17. Selection Sort 5 1 3 4 2 6 Comparison Data Movement Sorted
- 18. Selection Sort 5 1 3 4 2 6 Comparison Data Movement Sorted
- 19. Selection Sort 5 1 3 4 2 6 Comparison Data Movement Sorted
- 20. Selection Sort 5 1 3 4 2 6 Comparison Data Movement Sorted Largest
- 21. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted
- 22. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted
- 23. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted
- 24. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted
- 25. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted
- 26. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted
- 27. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted Largest
- 28. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted
- 29. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted
- 30. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted
- 31. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted
- 32. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted
- 33. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted Largest
- 34. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted
- 35. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted
- 36. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted
- 37. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted
- 38. Selection Sort 2 1 3 4 5 6 Comparison Data Movement Sorted Largest
- 39. Selection Sort 1 2 3 4 5 6 Comparison Data Movement Sorted
- 40. Selection Sort 1 2 3 4 5 6 Comparison Data Movement Sorted DONE!
- 41. 4240 2 1 3 3 4 0 -1 655843 40 2 1 43 3 4 0 -1 42 65583 40 2 1 43 3 4 0 -1 58 3 6542 40 2 1 43 3 65 0 -1 58 3 42 4 Selection Sort: Example
- 42. 4240 2 1 3 3 4 0 655843-1 42-1 2 1 3 3 4 0 65584340 42-1 2 1 3 3 4 655843400 42-1 2 1 0 3 4 655843403 Selection Sort: Example
- 43. 1 42-1 2 1 3 4 6558434030 42-1 0 3 4 6558434032 1 42-1 0 3 4 6558434032 1 420 3 4 6558434032-1 1 420 3 4 6558434032-1 Selection Sort: Example
- 44. Selection Sort: Analysis Running time: − Worst case: O(N2 ) − Best case: O(N2 )