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Clustering is one of the basic operations in data analysis, and the cluster structure of a dataset often has a marked effect on observed patterns in data. Testing whether a data mining result is implied by the cluster structure can give substantial information on the formation of the dataset. We propose a new method for empirically testing the statistical significance of patterns in real-valued data...
This paper proposes an improved K-Modes clustering method based on Chi-square statistics, using Chi-square statistics to characterize the relationship between the attributes of data objects. On this basis, the new distance measure is proposed, The distance measure method not only take into account the value of an attribute of an object different from itself, but also take into account other attributes'...
The clustering and recognition of Web video content play an important role in multimedia information retrieval. This paper proposes a method for both clustering and recognizing Web video content using a histogram of phoneme symbols (HoPS). HoPS contains information about speech and sound intervals. In this study, three experiments were conducted.The first experiment allocated HoPS feature of video...
The algorithm of locally adaptive clustering for high dimensional data (LAC) processes soft subspace clustering by local weightings of features. To solve the localization of LAC in specifying the number of clusters, this paper reworks the validity index for fuzzy clustering to evaluate the clustering results of LAC. Compared with real clustered data, the method is proved feasible. In the new algorithm,...
Remote sensing of terrestial and planetary surfaces in the infrared for the purpose of identifying minerals and their distribution is an ongoing activity. The huge amount of spectral data currently available and expected from future space missions presents a challenge to the scientist determine to extract useful scientific information from this data. Automated methods to facilitate this process are...
In this paper, the clustering analysis method is used in the optimization of observed gravity data during the computation of local geoid, while the two slopes of terrain data can be utilized as criteria. Further, a numerical experiment is carried out in hill area. Compared with the result from non-deleted observed gravity data, the deleted data can still work out acceptable result of similar precision...
As time series mining has become more prevalent and attracted much research interest, recent goals and efforts have been shifted toward scalability issue. One of the successful solutions is finding suitable representation of the data via dimensionality reduction. In this work, we introduce a novel fractal representation for time series data, which uses merely three real values to represent any time...
With rapid development of Internet information, It is quite an important project for data mining that how to classify these large amounts of texts. In this paper, we propose an improved text classify cluster algorithm, while calculating similarity, we synthetically consider the relationship between keywords and eigenvector representation on base of term frequency statistics, thereby it lessens sensitivity...
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