data cluster造句
例句與造句
- degree of data clustering with the index
使用索引的情況下數(shù)據(jù)群集的程度 - improved fuzzy kernal algorithm for gene expression data clustering
基因表達(dá)數(shù)據(jù)聚類中模糊核算法的改進(jìn) - cluster-pc based parallel distributed data clustering and its applications
基于集群的并行分布式聚類及其應(yīng)用 - then a new data clustering method based on pcnn is also presented, which is prospective and has a wide application
該方法構(gòu)思新穎、適用范圍廣、具有廣闊的應(yīng)用前景。 - association matrix between behavior and object is defined and subsystems are derived based on data clustering
定義了行為和對(duì)象的關(guān)聯(lián)度矩陣，基于聚類方法確定信息系統(tǒng)子系統(tǒng)的建設(shè)需求。 - It's difficult to find data cluster in a sentence. 用data cluster造句挺難的
- it first introduces the concept of data clustering and data classification . then it discusses eleven methods of cluster analysis
首先介紹聚類和分類的概念，然后討論了十一種數(shù)據(jù)聚類方法。 - furthermore we try to get a new approach by combining data clustering and outlier detection . discuss the sampling techniques frequently used in clustering and outlier detection
對(duì)聚類利異常檢測(cè)算法中大量使用的采樣技術(shù)進(jìn)行了討論，并提山了密度偏向的采樣技術(shù)。 - next an index system including 8 indexes to measure position knowledge amount is constructed, by which we performed classification on those positions by the statistical methods of data clustering and factor analysis
隨后歸納出衡量一般崗位知識(shí)含量的體系的8個(gè)指標(biāo)，用聚類和因子分析方法將各崗位按知識(shí)含量高低進(jìn)行分級(jí)。 - in this paper, we do a lot of experiments on artificial data and real world data, and make a comparison with classical data clustering methods . the experimental results prove its great advantage over others
本文中，我們將它與傳統(tǒng)的數(shù)據(jù)聚類方法分別對(duì)模擬數(shù)據(jù)和真實(shí)數(shù)據(jù)做以實(shí)驗(yàn)，并加以比較，證明了該方法優(yōu)于傳統(tǒng)方法，驗(yàn)證了它的優(yōu)良性。 - after that, the thesis proposes a algorithm to seek the degree of the texture of a digital image . 3rd . it gives a new model for data clustering, " village-town " model and a clustering globally best algorithm based on genetic method
以待聚類數(shù)據(jù)集為對(duì)象從尋求全局最優(yōu)配置入手，首次提出了基于遺傳算法的聚類變精度搜索方案和基于點(diǎn)分布密度的類合并準(zhǔn)則。 - clustering analysis is one of most heated research topic of the day . data clustering, a unsupervised classifying method, is the process of grouping together similar multi-dimensional data vectors into a number of clusters or bins
聚類就是把一個(gè)沒有類別標(biāo)記的樣本集按某種準(zhǔn)則劃分成若干類，使類內(nèi)樣本的相似性盡可能大，而類間樣本相似性盡量小，是一種無監(jiān)督的分類方法。 - at the aspect of preprocess, some preprocess methods are studied and improved, including rough set, data clustering, concept hierarchies and language field, etc . at the aspect of mining algorithms, classification is an important knowledge discovery method
在數(shù)據(jù)的預(yù)處理方面，主要研究粗集理論、數(shù)據(jù)聚類、概念樹、語言場(chǎng)等預(yù)處理方法。在挖掘模型與算法的選取中，分類是一種重要的知識(shí)發(fā)現(xiàn)方法，它能以簡(jiǎn)潔的模型預(yù)測(cè)新到達(dá)對(duì)象的類別。 - (4 ) seven kinds of spatial data clustering approaches are studied . and the technique to solve the problem of constraint-based spatial cluster analysis is explored . in addition, a new spatial clustering algorithm based on genetic algorithms is set forward and it can give attention to local constringency and the whole constringency
(4)系統(tǒng)研究了七種典型的空間數(shù)據(jù)聚類方法，積極探索基于約束條件的空間聚類問題的解決方案；將遺傳算法引入空間數(shù)據(jù)聚類領(lǐng)域，提出一種基于遺傳算法的空間聚類算法，該算法兼顧了局部收斂和全局收斂性能。 - in this thesis, several methods such as wavelet analysis, fuzzy theory, data clustering, neural network, model recognition and genetic algorithm are mentioned . and the author has made some improvement in fields of digital image analysis and encryption with genetic algorithm and wavelet analysis
在本文各章節(jié)中分別使用了小波分析、模糊數(shù)學(xué)、數(shù)據(jù)聚類、神經(jīng)網(wǎng)絡(luò)、模式識(shí)別和遺傳算法等技術(shù)，并對(duì)遺傳算法和小波分析在數(shù)字圖像分析與加密的應(yīng)用領(lǐng)域內(nèi)分別有所拓展。 - what is data mining is first discussed, including the emergence background and definition of data mining . then some important subjects of data mining at home and abroad are introduced, such as association rules, data generalization, data classification, data clustering etc . finally, some challenges in the research and application of data mining are discussed, which contribute to the advanced development of data mining
第一章首先介紹了什么是數(shù)據(jù)挖掘，包括數(shù)據(jù)挖掘的產(chǎn)生背景和定義，介紹了目前國內(nèi)外數(shù)據(jù)挖掘中研究的一部分重要內(nèi)容的概況，包括關(guān)聯(lián)規(guī)則、數(shù)據(jù)綜合和概括、數(shù)據(jù)分類、數(shù)據(jù)聚類等。
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