英语翻译 将下面的文字翻译成英文
2024-05-17来源：编辑
Data mining is found implied, novel, from the database on decision has the potential value of knowledge and rules of procedure, has now has been widely applied in many areas. Cluster analysis is one of the most important technology in the area of data mining, clustering is the collection of physical or abstract objects into multiple clusters of similar objects. Generated by clustering cluster is a collection of objects, similar to the objects to each other in the same cluster, different objects in a different family. And in the many clustering algorithms, clustering algorithm is the most classic K-means.
K-means clustering algorithm is a typical clustering algorithm based on partition, the algorithm has a simple, for large-scale data mining of high performance and scalability, advantages of close to linear time complexity. But there were disadvantages to the algorithm: algorithm sensitive to initial; initial value using stochastic, algorithm is not stable enough; algorithm into a local minimum, and generally only found globular clusters; number of clusters k need to be given in advance.
This article is mainly to introduce and analyze traditional clustering algorithm based on K-means and understand the advantages and disadvantages of clustering algorithm based on K-means, and improvements to clustering algorithm based on K-means. The improvement is intended primarily for k-means clustering algorithm to improve initial value dependency of this feature. Improved mainly through some initial point selection algorithms, thus overcoming shortcomings such as the K-means algorithm is not stable, and be able to make the clustering results more precise.
Study on the main work content and outcomes are as follows:
1. introduction and analysis of clustering algorithm based on K-means thought and realization of the algorithm. And then some data to understand the advantages and disadvantages of algorithms.
2. improvements to disadvantage of clustering algorithm based on K-means, primarily for k-means clustering algorithm to improve initial value dependency of this feature. Two improved methods, the first thought of using Huffman, the second reference to greedy algorithm and Kruskal algorithm for thoughts.
Data mining from a database found implied, novel, a potential value of decision-making process of the knowledge and rules in many areas, has been widely used. And clustering analysis is the most important data mining field technology of clustering analysis is put physics or abstract collections of objects into the object by similar composed of multiple cluster process. By clustering generated clusters are a group of collections of objects, the object in the same clusters resemble each other, different with different objects in the cluster. And in many clustering algorithms, K - means clustering algorithm is the most classic.
K - means algorithm is a kind of typical clustering algorithm based on division, this algorithm has thought is simple, and the mining of large-scale data with efficiency and scalability, time complexity close to linear, etc. But this algorithm also exists weakness: algorithm of initial sensitive; Using random initial value, the algorithm is not quite stable; Algorithm easily into the local minimum, and only commonly found globular clusters; The cluster number K need to be given.
This paper mainly introduces and analyses tradition K - means clustering algorithms and understand K - means clustering algorithm, and finally the advantages and disadvantages of K - means clustering algorithm was improved. This improvement mainly for K - means clustering algorithm's dependence on initial value this characteristic is improved. Improvement mainly through some algorithm of the initial points, so choose overcomes K - means algorithm unstable, and can make the disadvantages such as clustering results more precise.
Main content and research results are as follows: 1. Introduction and analysis K - means clustering algorithms, and realize the ideological algorithm. Then through some data to understand the advantages and disadvantages of this algorithm.
2. The K - means clustering algorithm improved the shortcomings, mainly for K - means clustering algorithm's dependence on initial value this characteristic is improved. Using the two improved methods for reference, the first kind, the second kind of reference Huffman thought Kruskal algorithm greedy algorithm of thoughts and ideas.
Data mining from a database found implied, novel, a potential value of decision-making process of the knowledge and rules in many areas, has been widely used. And clustering analysis is the most important data mining field technology of clustering analysis is put physics or abstract collections of objects into the object by similar composed of multiple cluster process. By clustering generated clusters are a group of collections of objects, the object in the same clusters resemble each other, different with different objects in the cluster. And in many clustering algorithms, K - means clustering algorithm is the most classic.
K - means algorithm is a kind of typical clustering algorithm based on division, this algorithm has thought is simple, and the mining of large-scale data with efficiency and scalability, time complexity close to linear, etc. But this algorithm also exists weakness: algorithm of initial sensitive; Using random initial value, the algorithm is not quite stable; Algorithm easily into the local minimum, and only commonly found globular clusters; The cluster number K need to be given.
This paper mainly introduces and analyses tradition K - means clustering algorithms and understand K - means clustering algorithm, and finally the advantages and disadvantages of K - means clustering algorithm was improved. This improvement mainly for K - means clustering algorithm's dependence on initial value this characteristic is improved. Improvement mainly through some algorithm of the initial points, so choose overcomes K - means algorithm unstable, and can make the disadvantages such as clustering results more precise.
Main content and research results are as follows:
1. Introduction and analysis K - means clustering algorithms, and realize the ideological algorithm. Then through some data to understand the advantages and disadvantages of this algorithm.
2. The K - means clustering algorithm improved the shortcomings, mainly for K - means clustering algorithm's dependence on initial value this characteristic is improved. Using the two improved methods for reference, the first kind, the second kind of reference Huffman thought Kruskal algorithm greedy algorithm of thoughts and ideas.
（那我用百度，嘻嘻）
Data mining is hidden from the database found, novel, potentially valuable decision-making knowledge and rules of procedure, has been widely in many fields of application. The cluster analysis is data mining one of the most important technology, clustering analysis is a collection of physical or abstract objects into objects by a similar process of multiple clusters. Clusters generated by clustering a set of objects is a collection of objects in the same cluster similar to each other, the different objects in different clusters. In many clustering algorithms, K-means clustering algorithm is the most classic.
K-means algorithm is a typical partition-based clustering algorithm, which has ideological and simple, large-scale data mining with high efficiency and scalability, time complexity close to linear and so on. But there are also disadvantages of the algorithm: the algorithm sensitive to initial values; initial randomized, algorithm is not stable enough; algorithm easy to fall into local minimum, and generally only found in globular clusters; K required number of clusters given in advance.
This article is to introduce and analyze the traditional K-means clustering algorithm and K-means clustering algorithm to understand the strengths and weaknesses, and finally on the K-means clustering algorithm is improved. Mainly for the improvement of K-means clustering algorithm dependent on the initial value to improve this feature. Mainly through the improvement of some of the initial site selection algorithm, so that K-means algorithm to overcome the disadvantage of instability, and to enable more accurate clustering results.
Main tasks and research results are as follows:
1. Introduction and analysis of K-means clustering algorithm ideas, and implement the algorithm. Then some data to understand the advantages and disadvantages of the algorithm.
2. On the K-means clustering algorithm to improve the shortcomings, mainly for K-means clustering algorithm dependent on the initial value to improve this feature. Improved using two methods, the first reference Huffman thought, the second reference greedy algorithm Kruskal algorithm thoughts and ideas.
Data mining from a database found implied, novel, a potential value of decision-making process of the knowledge and rules in many areas, has been widely used. And clustering analysis is the most important data mining field technology of clustering analysis is put physics or abstract collections of objects into the object by similar composed of multiple cluster process. By clustering generated clusters are a group of collections of objects, the object in the same clusters resemble each other, different with different objects in the cluster. And in many clustering algorithms, K - means clustering algorithm is the most classic.
K - means algorithm is a kind of typical clustering algorithm based on division, this algorithm has thought is simple, and the mining of large-scale data with efficiency and scalability, time complexity close to linear, etc. But this algorithm also exists weakness: algorithm of initial sensitive; Using random initial value, the algorithm is not quite stable; Algorithm easily into the local minimum, and only commonly found globular clusters; The cluster number K need to be given.
This paper mainly introduces and analyses tradition K - means clustering algorithms and understand K - means clustering algorithm, and finally the advantages and disadvantages of K - means clustering algorithm was improved. This improvement mainly for K - means clustering algorithm's dependence on initial value this characteristic is improved. Improvement mainly through some algorithm of the initial points, so choose overcomes K - means algorithm unstable, and can make the disadvantages such as clustering results more precise.
Main content and research results are as follows:
1. Introduction and analysis K - means clustering algorithms, and realize the ideological algorithm. Then through some data to understand the advantages and disadvantages of this algorithm.
2. The K - means clustering algorithm improved the shortcomings, mainly for K - means clustering algorithm's dependence on initial value this characteristic is improved. Using the two improved methods for reference, the first kind, the second kind of reference Huffman thought Kruskal algorithm greedy algorithm of thoughts and ideas.
希望对你有帮助
K-means clustering algorithm is a typical clustering algorithm based on partition, the algorithm has a simple, for large-scale data mining of high performance and scalability, advantages of close to linear time complexity. But there were disadvantages to the algorithm: algorithm sensitive to initial; initial value using stochastic, algorithm is not stable enough; algorithm into a local minimum, and generally only found globular clusters; number of clusters k need to be given in advance.
This article is mainly to introduce and analyze traditional clustering algorithm based on K-means and understand the advantages and disadvantages of clustering algorithm based on K-means, and improvements to clustering algorithm based on K-means. The improvement is intended primarily for k-means clustering algorithm to improve initial value dependency of this feature. Improved mainly through some initial point selection algorithms, thus overcoming shortcomings such as the K-means algorithm is not stable, and be able to make the clustering results more precise.
Study on the main work content and outcomes are as follows:
1. introduction and analysis of clustering algorithm based on K-means thought and realization of the algorithm. And then some data to understand the advantages and disadvantages of algorithms.
2. improvements to disadvantage of clustering algorithm based on K-means, primarily for k-means clustering algorithm to improve initial value dependency of this feature. Two improved methods, the first thought of using Huffman, the second reference to greedy algorithm and Kruskal algorithm for thoughts.
Data mining from a database found implied, novel, a potential value of decision-making process of the knowledge and rules in many areas, has been widely used. And clustering analysis is the most important data mining field technology of clustering analysis is put physics or abstract collections of objects into the object by similar composed of multiple cluster process. By clustering generated clusters are a group of collections of objects, the object in the same clusters resemble each other, different with different objects in the cluster. And in many clustering algorithms, K - means clustering algorithm is the most classic.
K - means algorithm is a kind of typical clustering algorithm based on division, this algorithm has thought is simple, and the mining of large-scale data with efficiency and scalability, time complexity close to linear, etc. But this algorithm also exists weakness: algorithm of initial sensitive; Using random initial value, the algorithm is not quite stable; Algorithm easily into the local minimum, and only commonly found globular clusters; The cluster number K need to be given.
This paper mainly introduces and analyses tradition K - means clustering algorithms and understand K - means clustering algorithm, and finally the advantages and disadvantages of K - means clustering algorithm was improved. This improvement mainly for K - means clustering algorithm's dependence on initial value this characteristic is improved. Improvement mainly through some algorithm of the initial points, so choose overcomes K - means algorithm unstable, and can make the disadvantages such as clustering results more precise.
Main content and research results are as follows: 1. Introduction and analysis K - means clustering algorithms, and realize the ideological algorithm. Then through some data to understand the advantages and disadvantages of this algorithm.
2. The K - means clustering algorithm improved the shortcomings, mainly for K - means clustering algorithm's dependence on initial value this characteristic is improved. Using the two improved methods for reference, the first kind, the second kind of reference Huffman thought Kruskal algorithm greedy algorithm of thoughts and ideas.
Data mining from a database found implied, novel, a potential value of decision-making process of the knowledge and rules in many areas, has been widely used. And clustering analysis is the most important data mining field technology of clustering analysis is put physics or abstract collections of objects into the object by similar composed of multiple cluster process. By clustering generated clusters are a group of collections of objects, the object in the same clusters resemble each other, different with different objects in the cluster. And in many clustering algorithms, K - means clustering algorithm is the most classic.
K - means algorithm is a kind of typical clustering algorithm based on division, this algorithm has thought is simple, and the mining of large-scale data with efficiency and scalability, time complexity close to linear, etc. But this algorithm also exists weakness: algorithm of initial sensitive; Using random initial value, the algorithm is not quite stable; Algorithm easily into the local minimum, and only commonly found globular clusters; The cluster number K need to be given.
This paper mainly introduces and analyses tradition K - means clustering algorithms and understand K - means clustering algorithm, and finally the advantages and disadvantages of K - means clustering algorithm was improved. This improvement mainly for K - means clustering algorithm's dependence on initial value this characteristic is improved. Improvement mainly through some algorithm of the initial points, so choose overcomes K - means algorithm unstable, and can make the disadvantages such as clustering results more precise.
Main content and research results are as follows:
1. Introduction and analysis K - means clustering algorithms, and realize the ideological algorithm. Then through some data to understand the advantages and disadvantages of this algorithm.
2. The K - means clustering algorithm improved the shortcomings, mainly for K - means clustering algorithm's dependence on initial value this characteristic is improved. Using the two improved methods for reference, the first kind, the second kind of reference Huffman thought Kruskal algorithm greedy algorithm of thoughts and ideas.
（那我用百度，嘻嘻）
Data mining is hidden from the database found, novel, potentially valuable decision-making knowledge and rules of procedure, has been widely in many fields of application. The cluster analysis is data mining one of the most important technology, clustering analysis is a collection of physical or abstract objects into objects by a similar process of multiple clusters. Clusters generated by clustering a set of objects is a collection of objects in the same cluster similar to each other, the different objects in different clusters. In many clustering algorithms, K-means clustering algorithm is the most classic.
K-means algorithm is a typical partition-based clustering algorithm, which has ideological and simple, large-scale data mining with high efficiency and scalability, time complexity close to linear and so on. But there are also disadvantages of the algorithm: the algorithm sensitive to initial values; initial randomized, algorithm is not stable enough; algorithm easy to fall into local minimum, and generally only found in globular clusters; K required number of clusters given in advance.
This article is to introduce and analyze the traditional K-means clustering algorithm and K-means clustering algorithm to understand the strengths and weaknesses, and finally on the K-means clustering algorithm is improved. Mainly for the improvement of K-means clustering algorithm dependent on the initial value to improve this feature. Mainly through the improvement of some of the initial site selection algorithm, so that K-means algorithm to overcome the disadvantage of instability, and to enable more accurate clustering results.
Main tasks and research results are as follows:
1. Introduction and analysis of K-means clustering algorithm ideas, and implement the algorithm. Then some data to understand the advantages and disadvantages of the algorithm.
2. On the K-means clustering algorithm to improve the shortcomings, mainly for K-means clustering algorithm dependent on the initial value to improve this feature. Improved using two methods, the first reference Huffman thought, the second reference greedy algorithm Kruskal algorithm thoughts and ideas.
Data mining from a database found implied, novel, a potential value of decision-making process of the knowledge and rules in many areas, has been widely used. And clustering analysis is the most important data mining field technology of clustering analysis is put physics or abstract collections of objects into the object by similar composed of multiple cluster process. By clustering generated clusters are a group of collections of objects, the object in the same clusters resemble each other, different with different objects in the cluster. And in many clustering algorithms, K - means clustering algorithm is the most classic.
K - means algorithm is a kind of typical clustering algorithm based on division, this algorithm has thought is simple, and the mining of large-scale data with efficiency and scalability, time complexity close to linear, etc. But this algorithm also exists weakness: algorithm of initial sensitive; Using random initial value, the algorithm is not quite stable; Algorithm easily into the local minimum, and only commonly found globular clusters; The cluster number K need to be given.
This paper mainly introduces and analyses tradition K - means clustering algorithms and understand K - means clustering algorithm, and finally the advantages and disadvantages of K - means clustering algorithm was improved. This improvement mainly for K - means clustering algorithm's dependence on initial value this characteristic is improved. Improvement mainly through some algorithm of the initial points, so choose overcomes K - means algorithm unstable, and can make the disadvantages such as clustering results more precise.
Main content and research results are as follows:
1. Introduction and analysis K - means clustering algorithms, and realize the ideological algorithm. Then through some data to understand the advantages and disadvantages of this algorithm.
2. The K - means clustering algorithm improved the shortcomings, mainly for K - means clustering algorithm's dependence on initial value this characteristic is improved. Using the two improved methods for reference, the first kind, the second kind of reference Huffman thought Kruskal algorithm greedy algorithm of thoughts and ideas.
希望对你有帮助
