Clustering Incomplete Data Research Articles

Comparison of incomplete data handling techniques for neuro-fuzzy systems

Real-life data sets sometimes miss some values. The incomplete data needs specialized algorithms or preprocessing that allows the use of the algorithms for complete data. The paper presents a comparison of various techniques for handling incomplete data in the neuro-fuzzy system ANNBFIS. The crucial procedure in the creation of a fuzzy model for the neuro-fuzzy system is the partition of the input domain. The most popular approach (also used in the ANNBFIS) is clustering. The analyzed approaches for clustering incomplete data are: preprocessing (marginalization and imputation) and specialized clustering algorithms (PDS, IFCM, OCS, NPS). The objective of our research is the comparison of the preprocessing techniques and specialized clustering algorithms to find the the most-advantageous technique for handling incomplete data with a neuro-fuzzy system. This approach is also the indirect validation of clustering.

Clustering Algorithm for Incomplete Data Sets with Mixed Numeric and Categorical Attributes

The traditional k-prototypes algorithm is well versed in clustering data with mixed numeric and categorical attributes, while it is limited to complete data. In order to handle incomplete data set with missing values, an improved k-prototypes algorithm is proposed in this paper, which employs a new dissimilarity measure for incomplete data set with mixed numeric and categorical attributes and a new approach to select k objects as the initial prototypes based on the nearest neighbors. The improved k-prototypes algorithm can not only cluster incomplete data with no need to impute the missing values, but also avoid randomness in choosing initial prototypes. To illustrate the accuracy of the established algorithm, traditional k-prototypes algorithm and k-prototypes employing the new dissimilarity measure are compared to the improved k-prototypes algorithm by using data from UCI machine learning repository. The experimental results show that the improved k-prototypes algorithm is superior to the other two algorithms with higher clustering accuracy.

A Fuzzy C-Means Approach for Incomplete Data Sets Based on Nearest-Neighbor Intervals

Partially missing data sets are a prevailing problem in pattern recognition. In this paper, the problem of clustering incomplete data sets is considered, and missing attribute values are imputed by the centers of corresponding nearest-neighbor intervals. Firstly, the algorithm estimates the nearest-neighbor intervals of missing attribute values by using the attribute distribution information of the data sets sufficiently. Secondly, the missing attribute values are imputed by the center of the intervals so as to clustering incomplete data sets. The proposed algorithm introduces the nearest neighbor information into incomplete data clustering, and the comparisons of the experimental results for two UCI data sets demonstrate the capability of the proposed algorithm.

A hybrid genetic algorithm–fuzzy c-means approach for incomplete data clustering based on nearest-neighbor intervals

Incomplete data are often encountered in data sets used in clustering problems, and inappropriate treatment of incomplete data can significantly degrade the clustering performance. In view of the uncertainty of missing attributes, we put forward an interval representation of missing attributes based on nearest-neighbor information, named nearest-neighbor interval, and a hybrid approach utilizing genetic algorithm and fuzzy c-means is presented for incomplete data clustering. The overall algorithm is within the genetic algorithm framework, which searches for appropriate imputations of missing attributes in corresponding nearest-neighbor intervals to recover the incomplete data set, and hybridizes fuzzy c-means to perform clustering analysis and provide fitness metric for genetic optimization simultaneously. Several experimental results on a set of real-life data sets are presented to demonstrate the better clustering performance of our hybrid approach over the compared methods.

Unsupervised learning of mixture models based on swarm intelligence and neural networks with optimal completion using incomplete data

In this paper, a new algorithm is presented for unsupervised learning of finite mixture models (FMMs) using data set with missing values. This algorithm overcomes the local optima problem of the Expectation-Maximization (EM) algorithm via integrating the EM algorithm with Particle Swarm Optimization (PSO). In addition, the proposed algorithm overcomes the problem of biased estimation due to overlapping clusters in estimating missing values in the input data set by integrating locally-tuned general regression neural networks with Optimal Completion Strategy (OCS). A comparison study shows the superiority of the proposed algorithm over other algorithms commonly used in the literature in unsupervised learning of FMM parameters that result in minimum mis-classification errors when used in clustering incomplete data set that is generated from overlapping clusters and these clusters are largely different in their sizes.

Missing Data Imputation Approach Based on Incomplete Data Clustering

Missing data processing is an important problem of data pre-processing in data mining field.Traditional missing data filling methods are mostly based on some statistical hypothesis,such as probability distribution,which might not be the most applicable approaches for data mining of large data set.Inspired by ROUSTIDA,an incomplete data analysis approach not using probability statistical methods,MIBOI is proposed for missing data imputation based on incomplete data clustering.Constraint Tolerance Set Dissimilarity is defined for incomplete data set of categorical variables,so the general dissimilarity of all the incomplete data objects in a set can be directly computed,and the missing data is imputed according to the incomplete data clustering results.The empirical tests using UCI benchmark data sets show that MIBOI is effective and feasible.

A Pseudo-EM Algorithm for Clustering Incomplete Longitudinal Data

A method for clustering incomplete longitudinal data, and gene expression time course data in particular, is presented. Specifically, an existing method that utilizes mixtures of multivariate Gaussian distributions with modified Cholesky-decomposed covariance structure is extended to accommodate incomplete data. Parameter estimation is carried out in a fashion that is similar to an expectation-maximization algorithm. We focus on the particular application of clustering incomplete gene expression time course data. In this application, our approach gives good clustering performance when compared to the results when there is no missing data. Possible extensions of this work are also suggested.

Cluster Analysis of Incomplete Microarray Data with Fuzzy Clustering

In this paper, we present a method for clustering incomplete Microarray data using alternating optimization in which a prior imputation method is not required. To reduce the influence of imputation in preprocessing, we take an alternative optimization approach to find better estimates during iterative clustering process. This method improves the estimates of missing values by exploiting the cluster Information such as cluster centroids and all available non-missing values in each iteration. The clustering results of the proposed method are more significantly relevant to the biological gene annotations than those of other methods, indicating its effectiveness and potential for clustering incomplete gene expression data.

Towards clustering of incomplete microarray data without the use of imputation

Clustering technique is used to find groups of genes that show similar expression patterns under multiple experimental conditions. Nonetheless, the results obtained by cluster analysis are influenced by the existence of missing values that commonly arise in microarray experiments. Because a clustering method requires a complete data matrix as an input, previous studies have estimated the missing values using an imputation method in the preprocessing step of clustering. However, a common limitation of these conventional approaches is that once the estimates of missing values are fixed in the preprocessing step, they are not changed during subsequent processes of clustering; badly estimated missing values obtained in data preprocessing are likely to deteriorate the quality and reliability of clustering results. Thus, a new clustering method is required for improving missing values during iterative clustering process. We present a method for Clustering Incomplete data using Alternating Optimization (CIAO) in which a prior imputation method is not required. To reduce the influence of imputation in preprocessing, we take an alternative optimization approach to find better estimates during iterative clustering process. This method improves the estimates of missing values by exploiting the cluster information such as cluster centroids and all available non-missing values in each iteration. To test the performance of the CIAO, we applied the CIAO and conventional imputation-based clustering methods, e.g. k-means based on KNNimpute, for clustering two yeast incomplete data sets, and compared the clustering result of each method using the Saccharomyces Genome Database annotations. The clustering results of the CIAO method are more significantly relevant to the biological gene annotations than those of other methods, indicating its effectiveness and potential for clustering incomplete gene expression data. The software was developed using Java language, and can be executed on the platforms that JVM (Java Virtual Machine) is running. It is available from the authors upon request.

Attribute weighted mercer kernel based fuzzy clustering algorithm for general non-spherical datasets

Clustering analysis is an important topic in artificial intelligence, data mining and pattern recognition research. Conventional clustering algorithms, for instance, the famous Fuzzy C-means clustering algorithm (FCM), assume that all the attributes are equally relevant to all the clusters. However in most domains, especially for high-dimensional dataset, some attributes are irrelevant, and some relevant ones are less important than others with respect to a specific class. In this paper, such imbalances between the attributes are considered and a new weighted fuzzy kernel-clustering algorithm (WFKCA) is presented. WFKCA performs clustering in a kernel feature space mapped by mercer kernels. Compared with the conventional hard kernel-clustering algorithm, WFKCA can yield the meaningful prototypes (cluster centers) of the clusters. Numerical convergence properties of WFKCA are also discussed. For in-depth studies, WFKCA is extended to WFKCA2, which has been demonstrated as a useful tool for clustering incomplete data. Numerical examples demonstrate the effectiveness of the new WFKCA algorithm

Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm

There is a trend in recent machine learning community to construct a nonlinear version of a linear algorithm using the 'kernel method', e.g. Support Vector Machines (SVMs), kernel principal component analysis, kernel fisher discriminant analysis and the recent kernel clustering algorithms. In unsupervised clustering algorithms using kernel method, typically, a nonlinear mapping is used first to map the data into a potentially much higher feature space, where clustering is then performed. A drawback of these kernel clustering algorithms is thatthe clustering prototypes lie in high dimensional feature space and hence lack clear and intuitive descriptions unless using additional projection approximation from the feature to the data space as done in the existing literatures. In this paper, a novel clustering algorithm using the 'kernel method' based on the classical fuzzy clustering algorithm (FCM) is proposed and called as kernel fuzzy c-means algorithm (KFCM). KFCM adopts a new kernel-induced metric in the data space to replace the original Euclidean norm metric in FCM and the clustered prototypes still lie in the data space so that the clustering results can be reformulated and interpreted in the original space. Our analysis shows that KFCM is robust to noise and outliers and also tolerates unequal sized clusters. And finally this property is utilized to cluster incomplete data. Experiments on two artificial and one real datasets show that KFCM hasbetter clustering performance and more robust than several modifications of FCM for incomplete data clustering.