What Is a Linked Data Structure?

A data structure is a way for a computer to store and organize data. A data structure is a collection of data elements that have one or more specific relationships with each other. Linked data structures generally have two interpretations: 1. Representing related data with a data structure, such as an associative array; 2. Linking related data structures through a method.

Linked data structures generally have two interpretations: 1. Represent related data with a data structure, such as

In computer science,

The data structure in computer software generally includes three aspects: the logical structure of data, the physical structure of data, and the operation of data. The logical structure of the data describes the logical relationship between the data, which can be represented by a two-tuple B = (K, R). Where K is a finite set of nodes, and the nodes (or elements) are discussed in the data structure Basic unit; R is a finite set of relations on K. The data structure is divided into linear structure and non-linear structure.

1. Linear structure. There is only one terminal node and one start node, and all nodes have at most one predecessor and one successor. Vector, stack, queue, and other sequential lists, and linear lists such as strings and linked lists are linear structures.

2. Non-linear structure. Tree structures, graphs, multi-dimensional arrays, sparse matrices, generalized tables, etc. are all non-linear structures.

The physical structure of data is the storage structure of the data, and the logical structure of the data is described in computer memory. There are generally four basic storage imaging methods:

1. A sequential approach. Logically adjacent nodes are stored in physically adjacent storage units. The relationship between the nodes is reflected by the adjacency relationship of the storage units. This method is mainly used for linear data structures. For non-linear structures, local linearization can also be used to achieve sequential storage. For example, in the tree structure, the nodes can be arranged into a sequence according to a certain rule, and the information inside the nodes is densely stored together using a sequential storage method, while other storage methods are used for the relationship between the nodes.

2. Linking method. The storage unit occupied by the node is divided into two parts, which respectively store data items and pointer items.

3. Indexing method. Use the index number of the node to determine the storage address of the node.

4. The method of hashing. Take the value of one or several fields _Wik as the key code in the field of node k. The storage address LOC (k) corresponding to node k is determined by the function f (called the hash function), and LOC (k) = f (W _ik ).

The operation of data is defined on the logical structure of the data, but the concrete implementation of the operation is performed on the physical structure. Data operations include searching, inserting, deleting, updating, and sorting nodes. There are still static and dynamic data structures. Static structure is the structure (logical structure and physical structure) characteristics of data that cannot be changed during the existence of the data structure, such as vectors, arrays, records, etc. The dynamic structure is that the structure characteristics of the data can be changed during the entire use period, such as stack, queue, linked list, tree, dynamic array structure, recursive data structure, etc.

Correlation analysis refers to finding frequent patterns, associations, correlations, or causal structures between item collections or object collections in transaction data, relational data, or other information carriers. In other words, association analysis is to find the connection between different commodities (items) in the transaction database.

Association analysis is a simple and practical analysis technique, which is to find the correlation or correlation existing in a large number of data sets, thereby describing the rules and patterns of the simultaneous occurrence of certain attributes in a thing. Association rules are generally used for analysis. Association rule discovery is looking for associations between things. For example, it can find the rules from a set of data items contained in a set of (assuming product purchase) transactions: if the purchase transaction contains item X and item Y, the item Z of the purchase transaction is included in N% of all purchase transactions . Sequence rule discovery is similar to association rule discovery, which is to find the sequence relationship between things. For example, each consumer of these transactions can be further identified from a set of data items contained in a group of transactions that the consumer is shopping for. Association rules generally have the following metrics:

Interest measure (interest measure): help users evaluate the resulting association rules. The degree of interest related to the evaluation of association rules includes simplicity, correctness, practicality, and novelty.

Conciseness is a measure of the complexity of a rule structure. Rules with complex structures are difficult to interpret and understand, which reduces their interest. Correctness measures are used to determine how convincing a rule is. Confidence Representation; a practicality metric is used to determine how likely the rule is to reappear, and it is expressed in support rules in association rules; a novelty metric is used to determine whether the rule is implied by another rule in the derived rule set to remove redundancy I rules.

Frequent itemset: The frequency of itemsets represents the number of transactions that contain itemsets. If the frequency of itemsets is greater than or equal to the product of the minimum support threshold and the total number of transactions in transaction data set D, the itemsets meet the minimum If the support threshold is required, the item set is a frequent itemset; the rest are called infrequent itemsets.

Strong association rules: Strong association rules refer to association rules that meet the user-defined minimum support threshold and minimum confidence threshold. In contrast, rules that do not meet the user-defined minimum support threshold and minimum confidence threshold are weak association rules ^[1] .