Categorical data analysis
Application of EM algorithm on missing categorical data analysis
... Algoritma EM merupakan salah satu daripada kaedah untuk menyelesaikan masalah berkaitan dengan data tidak lengkap berdasarkan kepada satu rangka lengkap. Algorithma EM merupakan satu pendekatan parametrik untuk ...
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Title: Clustering Categorical Data for Internet Security Applications: A Review
... For Categorical Link Based Clustering that Cluster ensembles are used as best alternative to the standard cluster ...The data set has been clustered by using any of the well known cluster algorithm and ...
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An Application of Categorical Analysis of Variance in Nested Arrangements
... (2007), categorical data analysis deals with data that classify an observation into one or more ...of categorical variables involves are variables whose data are on nominal or ...
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Categorical Data
... binary data; bivariate probit; cate- gorical data; choice-based sampling; conditional logit; count data; dis- crete outcome; extreme value distribution; index model; independence of irrelevant ...
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Bayesian Analysis of Linear and Nonlinear Latent Variable Models with Fixed Covariate and Ordered Categorical Data
... , respectively. H 0 diag ( 01 2 ,..., 0 2 p ), and 0 , 0 k , 0 k , 0 k , 0 k , 0 , 0 k , H H 0 , 0 k , and R 0 are assumed to be given by the prior information. In general, prior information ...
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Parity Progression Estimation and Categorical Analysis of Birth Cohort Data in Ghana: The Case of Birth Data Derived from Health Insurance Data
... through parity progression ratios (PPRs) and the use of survival and categorical analyses to determine the relationship among the categories. The Kaplan Meier statistics showed a significant difference among the ...
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Simplification of correspondence analysis for more precise calculation which one qualitative variables is two categorical data
... correspondence analysis (CA) are using the long stages matrix operations, so that through many times rounding process, and the eigenvalues obtained by numerical ...correspondence analysis (SoCA) of 2 × J ...
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Alzheimer’s Disease Prediction Model Using Demographics and Categorical Data
... neuro-pathological data have been used by many researchers [13] [15] [19] ...of data for instance, clinical diagnosis (CD), clinical judgment of symptoms (CJS) and minimum mental state examination (MMSE) is ...
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Imputation techniques for non-ordered categorical missing data
... missing data of continuous and normally distributed variables, but it was later used to impute missing values of categorical data which do not assume normality (Allison, ...and categorical ...
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Local indicators for categorical data : Impacts of scaling decisions
... The ability to capture small features is reduced as grain size is increased. Consider the mountain pine beetle case study, using the largest grain size (p = 275, [D] in Figure 3), remnant forest patches in the large ...
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A Novel Similarity Measure for Clustering Categorical Data Sets
... large data sets. However, k-means clustering algorithm fails to handle data sets with categorical attributes because it minimizes the cost function that is numerically ...numerical data sets. ...
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Survey on Outlier Detection Techniques Using Categorical Data
... audit data, network data, and clinical trial ...Every data point is assigned formulated outlier factor computed as sum of distance from its k nearest ...where data set get randomized for ...
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Continuation-ratio Model for Categorical Data: A Gibbs Sampling Approach
... Abstract—In this paper we discuss the continuation- ratio model for ordinal data. This particular type of model is to model the probability of one particu- lar category given the categories proceeding this one. It ...
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Ensemble Fuzzy Clustering for Mixed Numeric and Categorical Data
... In data mining, clustering is one of the major tasks and aims at grouping the data objects into meaningful classes (clusters) such that the similarity of objects within clusters is maximized, and the ...
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A Semi-Supervised Approach to the Detection and Characterization of Outliers in Categorical Data
... of data mining and the advances in machine learning that occurred in the 1990s, the research on anomaly detection gained new impetus and gave rise to many novel approaches and algorithms ...of data they ...
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ASSESSING SOME ESTIMATION CRITERIA OF MEASUREMENT ERROR FOR CATEGORICAL DATA
... The estimation of the measurement error is essential to study data quality in surveys. In this paper, some criteria to estimate of measurement error for categorical data are proposed based on the ...
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A Novel Approach in Multi Response Optimization for Correlated Categorical Data
... of categorical variable for that ...the analysis of association ...single categorical response, it is simpler and more natural to use logit ...for analysis of an experiment including control ...
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A Modified Overlapping Partitioning Clustering Algorithm for Categorical Data Clustering
... Text documents are considered as an important source of information. In order to allow effective text operations and better structuring of the documents, a pre-processing phase is required to reformat documents as it is ...
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Unsupervised Coupled Metric Similarity for Non-IID Categorical Data
... of categorical data, especially when data is embedded with complex relationships [11], ...Big data applications become increasingly important and popular, heterogeneous and hierarchical ...
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INFORMATION-THEORETIC OUTLIER DETECTION FOR LARGE-SCALE CATEGORICAL DATA
... the data distribution and attribute correlation to measure the similarity of outlier candidates in data sets and the efficiency results from the outlier factor function derived from the ...
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