Ultra dense networks（UDN） are treated as a promising technology to meet the challenges of the future wireless communications where interference plays an important role in the network performance. Interference alignm...
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Ultra dense networks（UDN） are treated as a promising technology to meet the challenges of the future wireless communications where interference plays an important role in the network performance. Interference alignment（IA） has been considered to be a resultful technique for achieving the optimal capacity scaling. However, in practical communication system, mitigating all interference via IA requires heavy signaling overhead and high iteration complexity. In this paper, we propose a dynamic clustering algorithm based on graph partitioning with low complexity. Our work focuses on dividing the whole network into a number of clusters under size constraint and realizes the maximum intra-cluster interference and minimum intercluster interference. In addition, neighbor selecting scheme based on neighbor interference ratio（NIR） in proposed algorithm can get the proper cluster result in the random spatial network model. Furthermore, proposed algorithm is compared with other traditional algorithms in complexity and performance. The simulation results show that proposed algorithm reduces the complexity of clustering process significantly and achieves average 7% higher performance gain than existing clustering algorithms.
In recent years, social network services have grown rapidly. The number of friends of each user using social network services has also increased significantly and is so large that clustering and managing these friends...
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In recent years, social network services have grown rapidly. The number of friends of each user using social network services has also increased significantly and is so large that clustering and managing these friends has become difficult. In this paper, we propose an algorithm called mCAF that automatically clusters friends. Additionally, we propose methods that define the distance between different friends based on different sets of measurements. Our proposed mCAF algorithm attempts to reduce the effort and time required for users to manage their friends in social network services. The proposed algorithm could be more flexible and convenient by implementing different privacy settings for different groups of friends. According to our experimental results, we find that the improved ratios between mCAF and SCAN are 35.8 % in similarity and 84.9 % in F-1 score.
With the rapid development of information technology, people are facing more and more data problems. The world of big data has quietly come to the world. Data analysis and processing has become one of the most importa...
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With the rapid development of information technology, people are facing more and more data problems. The world of big data has quietly come to the world. Data analysis and processing has become one of the most important research topics at present. In the process of data research, the processing method of large data is particularly important, especially the data mining technology has become the most important. In the processing of large data, clustering analysis is an indispensable means. It is widely used in many fields, such as medical diagnosis, image processing, pattern recognition, data analysis, data mining, decision support and marketing. Because the clustering algorithm is relatively simple, easy to realize and has good application effect in practical application, it has also attracted extensive attention of many experts and scholars. In this paper, based on the basic principle and application of clustering algorithm under the background of big data on the previous experience, the students of Yunnan Minzu University consumption expenditure data, and cluster analysis was performed on the data using Matlab software to analyze the consumption level of different types of students and what the students the consumption level.
Aiming at the recommendation of music field, this paper proposes a music recommendation algorithm based on attribute selection and application clustering. Firstly, the recommended progress of music conduct in-depth an...
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Aiming at the recommendation of music field, this paper proposes a music recommendation algorithm based on attribute selection and application clustering. Firstly, the recommended progress of music conduct in-depth analysis to focus on building properties of music and the problems of interaction in the field of music recommendation. With clustering algorithm as the main method, more accurate clustering will make the recommendation more precise. Based on attribute building and clustering, the overall recommendation scheme is designed, and the music is clustered by attribute judgment. Experimental results show that music recommendation proposed algorithm has a better recommendation effect, can effectively improve the user experience.
In this paper, two clustering algorithms are proposed: DBSCAN Entropy-based (DBSCAN) and dynamic clustering algorithm (DBSCAN) to determine the optimal clustering results. The Optimal Number of Clusters ENDBSCAN (OPEN...
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In this paper, two clustering algorithms are proposed: DBSCAN Entropy-based (DBSCAN) and dynamic clustering algorithm (DBSCAN) to determine the optimal clustering results. The Optimal Number of Clusters ENDBSCAN (OPENDBSCAN). ENDBSCAN takes information entropy as the main consideration in clustering, and avoids the traditional DBSCAN algorithm needs to define two parameters of Eps (neighborhood radius) and Minpts (density threshold). At the same time, in order to solve the problem of huge amount of data, a data preprocessing method is proposed. The method divides the data into blocks and divides them into different computer nodes, so as to make full use of the data nodes. Computing power, and improve the efficiency and scalability of the clustering algorithm. OP-ENDBSCAN is an algorithm to determine the optimal number of clustering dynamically and to evaluate the quality of clustering. Based on the analysis of ENDBSCAN, it is found that this algorithm needs to determine the number of clustering by artificially. In order to avoid this problem, OPENDBSCAN The effect of anthropogenic parameters on the clustering results was improved and the quality of clustering was improved. Experiments show that both ENDBSCAN and OP-ENDBSCAN can show high efficiency under different data sets and show good clustering results.
Electrical equipment's family defects are common deficiencies usually caused by some particular factors such as the material of equipment and the process of design and manufacture. In order to classify these defec...
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ISBN:
(纸本)9781467390682
Electrical equipment's family defects are common deficiencies usually caused by some particular factors such as the material of equipment and the process of design and manufacture. In order to classify these defect data, the paper proposes a clustering algorithm combines PAM and FCM. The method uses PAM firstly to generate the cluster prototypes With the goal of lowering the initial randomness of FCM, and then it runs FCM to obtain the final clustering results. These steps are expected to ensure the accuracy of the algorithm and take less iteration. Experiments using electrical equipment' s family defects data-sets to test and verity the accuracy and efficiency of the algorithm are discussed. The results show that the combination methods used in this paper provide a better performance in both accuracy and run time when compared with traditional analysis approach like hierarchical clustering algorithm.
With the completion of the human genome sequencing, a large number of data especially amino acid sequences floods into biological database, How to analyze these data quickly and even predict the structure and function...
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ISBN:
(纸本)9781467399043
With the completion of the human genome sequencing, a large number of data especially amino acid sequences floods into biological database, How to analyze these data quickly and even predict the structure and function of protein correctly have become hot topics in recent years, In this paper, we mainly study K-means clustering algorithm and KNN classifier in amino acid sequences of complicated data, which are applied in the prediction of protein sub-cellular localization. In many cases, fuzzy boundary and unbalance are frequently appeared among biological data. The accuracy will be lower, if we make a prediction through traditional KNN and K-means clustering algorithm directly. Firstly, in order to make clear the unbalance, we propose the within-class thought to make sure that training samples in each class around the testing sample are selected and we introduce membership to tell which class the testing sample belongs to. Then, we bring in rough sets and membership to solve the fuzzy boundary. Particularly, we apply correlation coefficient in the rough sets to better reflect the relationship among data objects. The experimental results based on protein sub-cellular localization prediction show that the methods proposed newly better work than the traditional methods.
The objective of this paper is to analyse the sad state of speech emotion using voice quality features. This will help the family members, relatives, well-wishers and medical practitioners for timely action to the nee...
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ISBN:
(纸本)9781509032570
The objective of this paper is to analyse the sad state of speech emotion using voice quality features. This will help the family members, relatives, well-wishers and medical practitioners for timely action to the needy person before onset of deep depression that may danger his/her life. Fuzzy C-means and K-means clustering algorithm have been used to put a boundary between sad speech state against the neutral utterances using voice quality features such as jitter, shimmer, noise to harmonic ratio (NHR) and harmonic to noise ratio (HNR). Shimmer has shown highest accuracy among all these features for sad state followed by jitter as the result suggest. However, for neutral utterances the accuracy of HNR features is best among all followed by shimmer.
Distance-based and density-based clustering algorithms are often used on large spatial and arbitrary shape of data sets. However, some well-known clustering algorithms have troubles when distribution of objects in the...
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ISBN:
(纸本)9781509052639
Distance-based and density-based clustering algorithms are often used on large spatial and arbitrary shape of data sets. However, some well-known clustering algorithms have troubles when distribution of objects in the dataset varies, and this may lead to a bad clustering result. Such bad performances are more dramatically significant on high-dimensional dataset. Recently, Rodriguez and Laio proposed an efficient clustering algorithm [1] based on two essential indicators: density and distance, which are used to find the cluster centers and play an important role in the process of clustering. However, this algorithm does not work well on high dimensional data sets, since the threshold of cluster centers has been defined ambiguously and hence it has to be decided visually and manually. In this paper, an alternative definition of the indicators is introduced and the threshold of cluster centers is automatically decided by using an improved Canopy algorithm. With fixed centers (each represents a cluster), each remaining data object is assigned to a cluster dependently in a single step. The performance of the algorithm is analyzed on several benchmarks. The experimental results show that (1) the clustering performance on some high dimensional data sets, e.g., intrusion detection, is better;and (2) on low dimensional data sets, the performances are as good as the traditional clustering algorithms.
Many experiments show that outliers have important implications for clustering. However, Most of the clustering algorithm ignores to compute outliers, or does not detect outliers well. In this paper, we present a loca...
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Many experiments show that outliers have important implications for clustering. However, Most of the clustering algorithm ignores to compute outliers, or does not detect outliers well. In this paper, we present a local deviation factor graph-based (LDFGB) algorithm. We measure the effectiveness of the algorithm by detection rate, false positive rate, false negative rate, time overhead, and so on. This algorithm can accurately detect outliers by calculating the relative distance between the data nodes. It can detect any shape of the cluster and still keep high detection rate for detecting known and unknown attacks. Using KDD CUP99 data sets, the experimental results show that this method is effective for improving the detection rates and false positive rates.
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