数据挖掘论文怎么找数据

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热心网友 时间：2022-06-01 22:03

UCI，数据堂~

热心网友 时间：2022-06-01 22:03

One, summary of content (according to chapter)
The first chapter: data structures and algorithms
This chapter is mainly to data, data type, data structure, algorithm and algorithm analysis of the basic concepts such as master, and how to reasonably organize data, process the data efficiently is expanding field of computer, the key to improve the efficiency of software, so the understanding of these concepts are very important.
Data refers to describe the value of objective things, characters, symbols, etc all can be input into the computer and processed by a computer program of the floorboard of the symbol, the basic unit is a data element, and the data type is a collection of similar values and define a set of operations on the value set. In advanced program language defines a data type, the compiler compiler system can get the following information: (1), a set of properties the same value; (2), a storage reservation system; (3), defined in this value set on a set of collections. The relationship between the structure data refers to the element, it includes logic structure, storage structure and data set is a set of operations; Logical structure of data (i.e., data structure) is divided into linear and nonlinear structure, and data storage methods: sequential storage methods, storage method of the connection and the storage method of the index and the storage method of the hash. Then there is related concepts about algorithm, algorithm is to solve a specific problem and to determine the limited collection of steps, it is finite, the certainty, feasibility, input and output. On algorithm performance analysis, performance analysis, performance analysis and space is divided into time here remember common time complexity comparison: O (1) < O (log n) < O (n) < nlog (n) < O (n) < O (n) < O (n) < O (2).
Chapter 2: the order table and its application
Order list, as a kind of simple and commonly used data structure, wider application scope, this chapter is mainly to order table, sequence table structure, data types, and basic algorithms and the related applications are introced.
First order sheet is a simple and commonly used data structure, its application range is relatively wide, its basic operation including long initialization table, table, element in the lookup table and remove elements, etc. To insert and delete operation in it, need a lot of mobile elements, the algorithm's time complexity is O (n). Second is about the search operation, mainly have a simple sequential search search search methods, such as, binary search, and blocking. To block search efficiency is optimal, but must be completed in order in the table lookup operation. Next is about sorting algorithms, sorting method usually has a direct insertion sort, hill sort, bubble sort, quick sort, selection sort and merge sort directly, their time complexity algorithm is as follows:
Sorting method is the average time complexity in the worst time complexity secondary storage space
Direct insertion sort O (n)
O (n)
O (1)
Hill sort O (n)
O (n)
O (1)
Bubble sort O (n)
O (n2) to O (1)
Quick sort O nlog (n)
O (n)
O nlog (n)
Direct selection sort O (n)
O (n)
O (1)
Merge sort O nlog (n)
O nlog (n)
O (n)
As can be seen from the table above,
1, in terms of time performance, better quick sort and merge sort, quick sort in the worst case time performance is inferior to merge sort.
2, although merge sort of time performance is very good, but it is most needed secondary storage space, performance space is poorer.
3, from the method of stability, time performance good internal sorting methods such as quick sort, hill sorting is not stable.
Chapter iii: list and its application
Linked list is a simple and commonly used data structure, compared with the sequential table, insert, delete nodes do not need to move elements, without prior estimate storage size advantages of flexible operation. It has six basic operations: (1), empty table table, o (2) (3), by the elements of a sequence number (4), by value lookup (5), insert, delete, (6).
Singly linked lists which every node has only one chain table pointer field, used to store its immediate successor storage location. But this makes the nodes on the elements of the operation is very difficult, so in each node to add a pointer to its precursor node domain and thus constitute a two-way linked list. At the same time because each nodes address is stored in the precursor of subsequent pointer, and stored in its successive nodes precursor pointer domains, so the two-way chain table insert operation is divided into before and after insertion.
The fourth chapter: the stack and application
Must first understand the stack is a restricted linear structure, adhere to the \"advanced\" rule, the insert and delete operations are at the top of the stack.
Secondly, according to a sequential storage and link storage stack is divided into sequential and chain stack. Stack stack which order is to use address continuous storage in storage data elements in the stack, and record the position of the current stack data elements; Basic algorithm including empty stack, stack is empty, sentenced, take full stack element, into and out of the stack. While use the chain chain stack storage stack data elements, data elements and record the current stack location; Each node data include data domain: used to store the value of the data element, the next pointer domains: used to store its immediate successor nodes store address, the basic operation and order the same stack.
The last is about the stack used: (1), value transformation problem; In converting decimal number N d hexadecimal number, get the remainder of the first is the lowest d hexadecimal number, display results at the last output; And finally obtain the remainder is the highest d hexadecimal number, need to the output. Out \"after the stack\" into the first property is consistent, so the available stack to store the remainder obtained by successive, and then output. (2), parentheses matching problem; When reading an expression, once read parentheses it into the stack, and read the next when compared with the stack in parentheses, brackets, if match, is out of the stack, or continue to read the expression. In the end, if the stack is empty stack, specification matching brackets, parentheses are not matching.
The fifth chapter: queue and its application
First and stack, need to know the queue is a restricted linear structure, abide by the \"fifo\" rule, the insert in the next morning, delete the adversary.
Secondly according to the sequential storage and chain store, the queue is divided into the order queue queue and chain. The order queue is to use address continuous vector space of the storage elements in the queue, in turn at the same time, record the current adversary yuan and the position of the element in the vector. It is worth noting that the circular queue in the order of \"false overflow\" phenomenon, namely, when the rear = maxlen - 1, that team is full. But with the right elements to ceaselessly, unit data array will be empty, is not necessarily true team full at this time. And full conditions for: Q - > front = = (Q - > rear + 1) % maxlen, the air condition is: Q - > front = = Q - > rear. Queue, and then the chain that is in the memory footprint of arbitrary, continuous or discontinuous physical storage area, using dynamic node spatial distribution; In it, it is worth noting that the chain queue team full do not exist.
Chapter 6: special matrix, generalized table and its application
First is about the concept of matrix storage method that;
1, two-dimensional array elements aij address is: (1), is given priority to with line sequence storage, Loc = Loc (a) + (a) * (m + 1) + [j] I * d (2), mainly the column sequence storage, Loc = Loc (a) (a) + [j] (I) * (n + 1) + * d, which m for the row number, n is the number of columns, d for each element of the number of storage units.
Under 2, symmetric matrices, the triangle is stored in a one-dimensional array sa [k], where 0 or less k < (n + 1) / 2; When I p j, k = I * (I + 1) / 2 + j, when I < j, k = j * (j + 1) / 2 + I
: 3, triangular matrix and symmetric matrix storage ideas as sa [k] using a one-dimensional array to store, and if the upper triangular matrix (lower triangular elements are the constant c), then when I p j, k = I * (I + 1) / 2 + j, when I < j, k = n * (n + 1) / 2; If lower triangular matrix (upper triangular elements are the constant c), then when I acuities were j, k = I * (n - 2 I + 1) / 2 + j - I, when I > j, k = n * (n + 1) / 2
4, diagonal matrix, the same are stored in a one-dimensional array sa [k], I + j k = 2
5, sparse matrix, the matrix of china-africa number of matrix element is much higher than the number of zero elements, triple table storage are available, and will value of the nonzero element with the alts, column number stored in together.
The second is about the concept of generalized table; Generalized table is n (n is 0 or higher) element a, a, a,... And a finite sequence of ai or atom or a generalized list, so generalized table is a recursive definition.
Chapter 7: binary tree and its application
First of all about the concept of binary tree and its properties; Binary tree is by n (n is 0 or higher) a finite set of nodes, including: (1), when n = 0, empty tree (2), when n > 0, the one and only one, specific nodes become binary tree root. In it there are two kinds of special binary tree, binary tree and binary tree completely. Full binary tree is a binary tree satisfy the following conditions: (1), Ren Yifei leaf nodes have two children (2), for any binary tree layer, if the plane has a node has children, all nodes on the layer to have children; The complete binary tree is in full binary tree is the most lower level from right to left continuous to delete several nodes of a binary tree. Binary tree at the same time has the following five characteristics: (1), on the binary tree of the ith layer up to 2 (I - 1) a node (I > 0) and (2), the binary tree of depth of k at most 2 (k) (k > 0) - 1 nodes (3), to any a non-empty binary tree, if its leaf nodes is n, degree of 2 nodes for n,, n = n + 1 (4), the complete binary tree with n nodes (n > 0) the height of ∟ log2n \"+ 1 (5), if the full binary tree or complete binary tree according to the\" from top to bottom, from left to right, each layer of root node Numbers for 1 \"of the mode number, node of the Numbers for the I, it's two children nodes number 2 I and I + 1, respectively, its parent node Numbers for I / 2.
Followed by the binary tree storage structure; Similar to the linear structure, it is also divided into sequential storage and link storage. Sequential storage sequence according to the numbering in the complete binary tree is, in turn, is stored in the one dimensional array. Such storage can be found easily around any node's parent and children, but for general binary tree can be quite a waste of space, and when the insert or delete nodes need a large number of mobile nodes, not concive to the realization of arithmetic. Then has drawn out the link storage of binary tree, each node including three domains, domain lchild Pointers: record the node address, rchild pointer left children domain: record the node right child's address, the data domain: store the information of the node.
Followed by binary tree traversal and clues, not only to the binary tree traversal, clues to the operation, but also should be able to iterate through the result according to the binary tree is constructed. In clues, to know each node ltag = 0 means lchild domains pointing to the node's left child, ltag = 1 says lchild domain point to the node of the precursor, rtag = 0 means rchild domains pointing to the node's right child, rtag = 1 says rchild domains pointing to the subsequent nodes.
Finally the application of binary tree, for example: Huffman tree for data compression provides a method, the binary sort tree, namely in the sequence traversal is the result of the increasing of orderly sequence.
Chapter 8: trees and forest and its application
First is about the related concepts of trees and forests and storage structure; Trees or between forest and binary tree there is a one-to-one correspondence relationship naturally, any a forest or a tree can be uniquely corresponding to a binary tree; On the other hand, any a binary tree can be uniquely corresponds to a forest or a tree. Here, to how the trees or forest converted into a binary tree, binary tree transformation spanning tree or forest. For tree order storage structure: parents notation, linked storage structure: (1) the representation, (2), baby brother notation, only need to know.
Followed by traversal of the tree and forest tree to know only the first sequence traversal and post-order traversal, the forest is only in the first sequence traversal and sequence traversal, and the first order (1), tree traversal sequence traversal of binary tree, and (2) the same, the post-order traversal of the tree and binary tree in the same sequence traversal (3), forest in the first sequence traversal and sequence traversal sequence traversal of binary tree, and, respectively, and in the same sequence traversal results.
Finally is a typical application of the tree -- B tree, it is a balance of multiple search trees, the study is based on the instance again algorithm, can understand algorithm.
Chapter 9: hash structure and its application
Hash structure of keyword as the independent variable is a storage node, by determining the function of H (that is, the hash function or hash function) is calculated, the desires of the function values as the nodes of the store address, and the nodes or keywords stored in the address, here the point is to grasp the hash and conflict handling these two concepts.
First is the hash, mainly the structure of the hash function. (1), direct addressing method: H = a * key (key) + b (2), in addition to leave remainder method: H = key (key) mod p, the p < = m, m for hash length (3), the numerical analysis method, analysis of key words in the collection each keyword in the distribution of each digital, find out the key digital distribution uniformity of several storage address (4), in the square: keyword square, will choose one among several Numbers as hash address (5), folding method: dividing the keyword into digits equal parts, pick up the parts as the sum of the keyword hash address.
The second is the conflict handling; Due to the hash function is likely to be different the same keyword calculated hash address, therefore need for conflict and keyword nodes to find a \"empty\" hash address. Conflict with method 1, open addressing method: Hi = (H + di (key)) mod m, I = 1, 2, 3,... M or less, K (K - 1) such as (1) detection, linear hash again to take di = 1, 2, 3,... , m - 1 (2), secondary detection hash again, di = 1 (2), 1 (2), 2 (2), 2 (2),... (3), pseudo random detection hash again to take di = pseudo random number; 2, chain address method: in a hash table of each add a pointer field in the storage unit, the conflict of the keywords in the chain table structure stored in a pointer to the unit.
Chapter 10: the figure and its application
First is the figure of related concepts; Graph is a data structure, Graph can be expressed in binary group, formalized definition is: the Graph (V, VR), including V = {x | x ∈ dataobject}, R = {VR}, VR = {< x > y P (x, y) Sunday afternoon (x, y) ∈ V}. Vertex, indegree and outdegree of degree, the number of vertices V as head of the arc is called V into degrees, with the number of vertices V for tail arc called V degrees, and the sum of the degree and the degree of vertices V.
The second is that figure storage structure; (1), the adjacency matrix: if < vi, vj > or (vi, vj) ∈ E (G), A (I, j) = 1; Conversely, A (I, j) = 0 (2) and adjacency list: each node corresponds to A single table in the figure, the ith A singly linked list of nodes attached to the vertex vi, the edge of each node is composed of three domains: adjvex adjacency point domain indicating and vertex vi adjacent vertices in the graph of the position, under the instructions attached to the vertex vi nextarc chain domain node of an edge or arc, the info stored information domain edge or arc related information.
The final graph traversal and figure of typical applications; To traverse the graph depth first algorithm or breadth-first algorithm, minimum spanning tree algorithm of premium or kruskal's algorithm, the shortest path of di jie tesla's algorithm and Freud and directed acyclic graph topology sorting algorithms, all need according to the algorithm instance walk again, so as to master these algorithms.

热心网友 时间：2022-06-01 22:03

UCI，数据堂~

热心网友 时间：2022-06-01 22:03

One, summary of content (according to chapter)
The first chapter: data structures and algorithms
This chapter is mainly to data, data type, data structure, algorithm and algorithm analysis of the basic concepts such as master, and how to reasonably organize data, process the data efficiently is expanding field of computer, the key to improve the efficiency of software, so the understanding of these concepts are very important.
Data refers to describe the value of objective things, characters, symbols, etc all can be input into the computer and processed by a computer program of the floorboard of the symbol, the basic unit is a data element, and the data type is a collection of similar values and define a set of operations on the value set. In advanced program language defines a data type, the compiler compiler system can get the following information: (1), a set of properties the same value; (2), a storage reservation system; (3), defined in this value set on a set of collections. The relationship between the structure data refers to the element, it includes logic structure, storage structure and data set is a set of operations; Logical structure of data (i.e., data structure) is divided into linear and nonlinear structure, and data storage methods: sequential storage methods, storage method of the connection and the storage method of the index and the storage method of the hash. Then there is related concepts about algorithm, algorithm is to solve a specific problem and to determine the limited collection of steps, it is finite, the certainty, feasibility, input and output. On algorithm performance analysis, performance analysis, performance analysis and space is divided into time here remember common time complexity comparison: O (1) < O (log n) < O (n) < nlog (n) < O (n) < O (n) < O (n) < O (2).
Chapter 2: the order table and its application
Order list, as a kind of simple and commonly used data structure, wider application scope, this chapter is mainly to order table, sequence table structure, data types, and basic algorithms and the related applications are introced.
First order sheet is a simple and commonly used data structure, its application range is relatively wide, its basic operation including long initialization table, table, element in the lookup table and remove elements, etc. To insert and delete operation in it, need a lot of mobile elements, the algorithm's time complexity is O (n). Second is about the search operation, mainly have a simple sequential search search search methods, such as, binary search, and blocking. To block search efficiency is optimal, but must be completed in order in the table lookup operation. Next is about sorting algorithms, sorting method usually has a direct insertion sort, hill sort, bubble sort, quick sort, selection sort and merge sort directly, their time complexity algorithm is as follows:
Sorting method is the average time complexity in the worst time complexity secondary storage space
Direct insertion sort O (n)
O (n)
O (1)
Hill sort O (n)
O (n)
O (1)
Bubble sort O (n)
O (n2) to O (1)
Quick sort O nlog (n)
O (n)
O nlog (n)
Direct selection sort O (n)
O (n)
O (1)
Merge sort O nlog (n)
O nlog (n)
O (n)
As can be seen from the table above,
1, in terms of time performance, better quick sort and merge sort, quick sort in the worst case time performance is inferior to merge sort.
2, although merge sort of time performance is very good, but it is most needed secondary storage space, performance space is poorer.
3, from the method of stability, time performance good internal sorting methods such as quick sort, hill sorting is not stable.
Chapter iii: list and its application
Linked list is a simple and commonly used data structure, compared with the sequential table, insert, delete nodes do not need to move elements, without prior estimate storage size advantages of flexible operation. It has six basic operations: (1), empty table table, o (2) (3), by the elements of a sequence number (4), by value lookup (5), insert, delete, (6).
Singly linked lists which every node has only one chain table pointer field, used to store its immediate successor storage location. But this makes the nodes on the elements of the operation is very difficult, so in each node to add a pointer to its precursor node domain and thus constitute a two-way linked list. At the same time because each nodes address is stored in the precursor of subsequent pointer, and stored in its successive nodes precursor pointer domains, so the two-way chain table insert operation is divided into before and after insertion.
The fourth chapter: the stack and application
Must first understand the stack is a restricted linear structure, adhere to the \"advanced\" rule, the insert and delete operations are at the top of the stack.
Secondly, according to a sequential storage and link storage stack is divided into sequential and chain stack. Stack stack which order is to use address continuous storage in storage data elements in the stack, and record the position of the current stack data elements; Basic algorithm including empty stack, stack is empty, sentenced, take full stack element, into and out of the stack. While use the chain chain stack storage stack data elements, data elements and record the current stack location; Each node data include data domain: used to store the value of the data element, the next pointer domains: used to store its immediate successor nodes store address, the basic operation and order the same stack.
The last is about the stack used: (1), value transformation problem; In converting decimal number N d hexadecimal number, get the remainder of the first is the lowest d hexadecimal number, display results at the last output; And finally obtain the remainder is the highest d hexadecimal number, need to the output. Out \"after the stack\" into the first property is consistent, so the available stack to store the remainder obtained by successive, and then output. (2), parentheses matching problem; When reading an expression, once read parentheses it into the stack, and read the next when compared with the stack in parentheses, brackets, if match, is out of the stack, or continue to read the expression. In the end, if the stack is empty stack, specification matching brackets, parentheses are not matching.
The fifth chapter: queue and its application
First and stack, need to know the queue is a restricted linear structure, abide by the \"fifo\" rule, the insert in the next morning, delete the adversary.
Secondly according to the sequential storage and chain store, the queue is divided into the order queue queue and chain. The order queue is to use address continuous vector space of the storage elements in the queue, in turn at the same time, record the current adversary yuan and the position of the element in the vector. It is worth noting that the circular queue in the order of \"false overflow\" phenomenon, namely, when the rear = maxlen - 1, that team is full. But with the right elements to ceaselessly, unit data array will be empty, is not necessarily true team full at this time. And full conditions for: Q - > front = = (Q - > rear + 1) % maxlen, the air condition is: Q - > front = = Q - > rear. Queue, and then the chain that is in the memory footprint of arbitrary, continuous or discontinuous physical storage area, using dynamic node spatial distribution; In it, it is worth noting that the chain queue team full do not exist.
Chapter 6: special matrix, generalized table and its application
First is about the concept of matrix storage method that;
1, two-dimensional array elements aij address is: (1), is given priority to with line sequence storage, Loc = Loc (a) + (a) * (m + 1) + [j] I * d (2), mainly the column sequence storage, Loc = Loc (a) (a) + [j] (I) * (n + 1) + * d, which m for the row number, n is the number of columns, d for each element of the number of storage units.
Under 2, symmetric matrices, the triangle is stored in a one-dimensional array sa [k], where 0 or less k < (n + 1) / 2; When I p j, k = I * (I + 1) / 2 + j, when I < j, k = j * (j + 1) / 2 + I
: 3, triangular matrix and symmetric matrix storage ideas as sa [k] using a one-dimensional array to store, and if the upper triangular matrix (lower triangular elements are the constant c), then when I p j, k = I * (I + 1) / 2 + j, when I < j, k = n * (n + 1) / 2; If lower triangular matrix (upper triangular elements are the constant c), then when I acuities were j, k = I * (n - 2 I + 1) / 2 + j - I, when I > j, k = n * (n + 1) / 2
4, diagonal matrix, the same are stored in a one-dimensional array sa [k], I + j k = 2
5, sparse matrix, the matrix of china-africa number of matrix element is much higher than the number of zero elements, triple table storage are available, and will value of the nonzero element with the alts, column number stored in together.
The second is about the concept of generalized table; Generalized table is n (n is 0 or higher) element a, a, a,... And a finite sequence of ai or atom or a generalized list, so generalized table is a recursive definition.
Chapter 7: binary tree and its application
First of all about the concept of binary tree and its properties; Binary tree is by n (n is 0 or higher) a finite set of nodes, including: (1), when n = 0, empty tree (2), when n > 0, the one and only one, specific nodes become binary tree root. In it there are two kinds of special binary tree, binary tree and binary tree completely. Full binary tree is a binary tree satisfy the following conditions: (1), Ren Yifei leaf nodes have two children (2), for any binary tree layer, if the plane has a node has children, all nodes on the layer to have children; The complete binary tree is in full binary tree is the most lower level from right to left continuous to delete several nodes of a binary tree. Binary tree at the same time has the following five characteristics: (1), on the binary tree of the ith layer up to 2 (I - 1) a node (I > 0) and (2), the binary tree of depth of k at most 2 (k) (k > 0) - 1 nodes (3), to any a non-empty binary tree, if its leaf nodes is n, degree of 2 nodes for n,, n = n + 1 (4), the complete binary tree with n nodes (n > 0) the height of ∟ log2n \"+ 1 (5), if the full binary tree or complete binary tree according to the\" from top to bottom, from left to right, each layer of root node Numbers for 1 \"of the mode number, node of the Numbers for the I, it's two children nodes number 2 I and I + 1, respectively, its parent node Numbers for I / 2.
Followed by the binary tree storage structure; Similar to the linear structure, it is also divided into sequential storage and link storage. Sequential storage sequence according to the numbering in the complete binary tree is, in turn, is stored in the one dimensional array. Such storage can be found easily around any node's parent and children, but for general binary tree can be quite a waste of space, and when the insert or delete nodes need a large number of mobile nodes, not concive to the realization of arithmetic. Then has drawn out the link storage of binary tree, each node including three domains, domain lchild Pointers: record the node address, rchild pointer left children domain: record the node right child's address, the data domain: store the information of the node.
Followed by binary tree traversal and clues, not only to the binary tree traversal, clues to the operation, but also should be able to iterate through the result according to the binary tree is constructed. In clues, to know each node ltag = 0 means lchild domains pointing to the node's left child, ltag = 1 says lchild domain point to the node of the precursor, rtag = 0 means rchild domains pointing to the node's right child, rtag = 1 says rchild domains pointing to the subsequent nodes.
Finally the application of binary tree, for example: Huffman tree for data compression provides a method, the binary sort tree, namely in the sequence traversal is the result of the increasing of orderly sequence.
Chapter 8: trees and forest and its application
First is about the related concepts of trees and forests and storage structure; Trees or between forest and binary tree there is a one-to-one correspondence relationship naturally, any a forest or a tree can be uniquely corresponding to a binary tree; On the other hand, any a binary tree can be uniquely corresponds to a forest or a tree. Here, to how the trees or forest converted into a binary tree, binary tree transformation spanning tree or forest. For tree order storage structure: parents notation, linked storage structure: (1) the representation, (2), baby brother notation, only need to know.
Followed by traversal of the tree and forest tree to know only the first sequence traversal and post-order traversal, the forest is only in the first sequence traversal and sequence traversal, and the first order (1), tree traversal sequence traversal of binary tree, and (2) the same, the post-order traversal of the tree and binary tree in the same sequence traversal (3), forest in the first sequence traversal and sequence traversal sequence traversal of binary tree, and, respectively, and in the same sequence traversal results.
Finally is a typical application of the tree -- B tree, it is a balance of multiple search trees, the study is based on the instance again algorithm, can understand algorithm.
Chapter 9: hash structure and its application
Hash structure of keyword as the independent variable is a storage node, by determining the function of H (that is, the hash function or hash function) is calculated, the desires of the function values as the nodes of the store address, and the nodes or keywords stored in the address, here the point is to grasp the hash and conflict handling these two concepts.
First is the hash, mainly the structure of the hash function. (1), direct addressing method: H = a * key (key) + b (2), in addition to leave remainder method: H = key (key) mod p, the p < = m, m for hash length (3), the numerical analysis method, analysis of key words in the collection each keyword in the distribution of each digital, find out the key digital distribution uniformity of several storage address (4), in the square: keyword square, will choose one among several Numbers as hash address (5), folding method: dividing the keyword into digits equal parts, pick up the parts as the sum of the keyword hash address.
The second is the conflict handling; Due to the hash function is likely to be different the same keyword calculated hash address, therefore need for conflict and keyword nodes to find a \"empty\" hash address. Conflict with method 1, open addressing method: Hi = (H + di (key)) mod m, I = 1, 2, 3,... M or less, K (K - 1) such as (1) detection, linear hash again to take di = 1, 2, 3,... , m - 1 (2), secondary detection hash again, di = 1 (2), 1 (2), 2 (2), 2 (2),... (3), pseudo random detection hash again to take di = pseudo random number; 2, chain address method: in a hash table of each add a pointer field in the storage unit, the conflict of the keywords in the chain table structure stored in a pointer to the unit.
Chapter 10: the figure and its application
First is the figure of related concepts; Graph is a data structure, Graph can be expressed in binary group, formalized definition is: the Graph (V, VR), including V = {x | x ∈ dataobject}, R = {VR}, VR = {< x > y P (x, y) Sunday afternoon (x, y) ∈ V}. Vertex, indegree and outdegree of degree, the number of vertices V as head of the arc is called V into degrees, with the number of vertices V for tail arc called V degrees, and the sum of the degree and the degree of vertices V.
The second is that figure storage structure; (1), the adjacency matrix: if < vi, vj > or (vi, vj) ∈ E (G), A (I, j) = 1; Conversely, A (I, j) = 0 (2) and adjacency list: each node corresponds to A single table in the figure, the ith A singly linked list of nodes attached to the vertex vi, the edge of each node is composed of three domains: adjvex adjacency point domain indicating and vertex vi adjacent vertices in the graph of the position, under the instructions attached to the vertex vi nextarc chain domain node of an edge or arc, the info stored information domain edge or arc related information.
The final graph traversal and figure of typical applications; To traverse the graph depth first algorithm or breadth-first algorithm, minimum spanning tree algorithm of premium or kruskal's algorithm, the shortest path of di jie tesla's algorithm and Freud and directed acyclic graph topology sorting algorithms, all need according to the algorithm instance walk again, so as to master these algorithms.