Clustering Mixed Data Research Articles

Longmixr: a tool for robust clustering of high-dimensional cross-sectional and longitudinal variables of mixed data types.

Accurate clustering of mixed data, encompassing binary, categorical, and continuous variables, is vital for effective patient stratification in clinical questionnaire analysis. To address this need, we present longmixr, a comprehensive R package providing a robust framework for clustering mixed longitudinal data using finite mixture modeling techniques. By incorporating consensus clustering, longmixr ensures reliable and stable clustering results. Moreover, the package includes a detailed vignette that facilitates cluster exploration and visualization. The R package is freely available at https://cran.r-project.org/package=longmixr with detailed documentation, including a case vignette, at https://cellmapslab.github.io/longmixr/.

Nonparametric Clustering of Mixed Data Using Modified Chi-Squared Tests.

We propose a non-parametric method to cluster mixed data containing both continuous and discrete random variables. The product space of the continuous and discrete sample space is transformed into a new product space based on adaptive quantization on the continuous part. Detection of cluster patterns on the product space is determined locally by using a weighted modified chi-squared test. Our algorithm does not require any user input since the number of clusters is determined automatically by data. Simulation studies and real data analysis results show that our proposed method outperforms the benchmark method, AutoClass, in various settings.

Clustering mixed type data: a space structure-based approach

Clustering mixed type data: a space structure-based approach

A hybrid k-prototypes clustering approach with improved sine-cosine algorithm for mixed-data classification

When dealing a classification problem with mixed data, most of conventional supervised learning algorithms cannot perform well due to their numerical characteristics. However, some clustering algorithms, such as k-prototypes algorithm, show their potential in clustering mixed data. Therefore, the current study intends to develop a novel clustering-based classification algorithm for mixed data to have both merits of classification and clustering. The proposed algorithm employs a sine-cosine algorithm (SCA) to find attribute weights and initial centroids for a k-prototypes algorithm. The objective function of the algorithm is formulated as a sum-up purity. To have better performance for SCA, a mutation strategy, containing Gaussian mutation, Cauchy mutation, Levy mutation, and single-point mutation, is embedded into the original SCA. The proposed algorithm is compared with some metaheuristic-based classification algorithms and existing classification algorithms. Based on the 10 data sets from UCI, the experimental results indicated that the proposed algorithm can achieve superior classification performance in terms of accuracy and Cohen's Kappa. In addition, mutation mechanism can make SCA have better performance.

Mixed Deep Gaussian Mixture Model: a clustering model for mixed datasets

Clustering mixed data presents numerous challenges inherent to the very heterogeneous nature of the variables. A clustering algorithm should be able, despite of this heterogeneity, to extract discriminant pieces of information from the variables in order to design groups. In this work we introduce a multilayer architecture model-based clustering method called Mixed Deep Gaussian Mixture Model that can be viewed as an automatic way to merge the clustering performed separately on continuous and non-continuous data. This architecture is flexible and can be adapted to mixed as well as to continuous or non-continuous data. In this sense we generalize Generalized Linear Latent Variable Models and Deep Gaussian Mixture Models. We also design a new initialisation strategy and a data driven method that selects the best specification of the model and the optimal number of clusters for a given dataset. Besides, our model provides continuous low-dimensional representations of the data which can be a useful tool to visualize mixed datasets. Finally, we validate the performance of our approach comparing its results with state-of-the-art mixed data clustering models over several commonly used datasets.

A Multi-View Clustering Algorithm for Mixed Numeric and Categorical Data

Clustering data with both numeric and categorical attributes is of great importance as such data are ubiquitous in real-world problems. Multi-view learning approaches have proven to be more effective and having better generalisation ability compared to single-view learning in many problems. However, most of the existing clustering algorithms developed for mixed numeric and categorical data are single-view. In this research, we propose a novel multi-view clustering algorithm based on the k-prototypes (which we term Multi-view K-Prototypes) for clustering mixed data. To the best of our knowledge, our proposed Multi-view K-Prototypes is the first multi-view version of the well-known k-prototypes algorithm. To cluster the mixed data over multiple views, we present a novel representation prototype of cluster centres in the scenario of multiple views, and we also devise formulas for updating the cluster centres over each view. Then we propose the concept of consensus cluster centres to output the final clustering result. Finally, we carried out a series of experiments on four benchmark datasets to assess the performance of the proposed Multi-view K-Prototypes clustering. Experimental results show that the Multi-view K-Prototypes algorithm outperforms the seven state-of-the-art algorithms in most cases.

Fuzzy Centroid and Genetic Algorithms: Solutions for Numeric and Categorical Mixed Data Clustering

Abstract Statistical data analysis in machine learning and data mining usually uses the clustering technique. However, data with both attributes or mixed data exists universally in real life. K-prototype is a well-known algorithm for clustering mixed data because of its effectiveness in handling large data. However, practically, k-prototype has two main weaknesses, the use of mode as a cluster center for categorical attributes cannot accurately represent the objects, and the algorithm may stop at the local optimum solution because affected by random initial cluster prototypes. To overcome the first weakness, we can use fuzzy centroid, and for second weakness is to implement the genetic algorithm to search the global optimum solution. Our research combines the genetic algorithm and Fuzzy K-Prototype to accommodate these two weaknesses. We set up two multivariate data with high correlation and low correlation to see the robustness of the proposed algorithm. According to four value indexes of clustering result evaluation, Coefficient Varians Index, Partition Coefficient, Partition Entropy, and Purity, show that our proposed algorithm has a better result than K prototype. Based on the evaluation result, we conclude that our proposed algorithm can solve two weaknesses of the k-prototype algorithm.

InitKmix-A novel initial partition generation algorithm for clustering mixed data using k-means-based clustering

Mixed datasets consist of both numeric and categorical attributes. Various k-means-based clustering algorithms have been developed for these datasets. Generally, these algorithms use random partition as a starting point, which tends to produce different clustering results for different runs. In this paper, we propose, initKmix, a novel algorithm for finding an initial partition for k-means-based clustering algorithms for mixed datasets. In the initKmix algorithm, a k-means-based clustering algorithm is run many times, and in each run, one of the attributes is used to create initial clusters for that run. The clustering results of various runs are combined to produce the initial partition. This initial partition is then used as a seed to a k-means-based clustering algorithm to cluster mixed data. Experiments with various categorical and mixed datasets showed that initKmix produced accurate and consistent results, and outperformed the random initial partition method and other state-of-the-art initialization methods. Experiments also showed that k-means-based clustering for mixed datasets with initKmix performed similar to or better than many state-of-the-art clustering algorithms for categorical and mixed datasets.

Metaheuristic-based possibilistic multivariate fuzzy weighted c-means algorithms for market segmentation

This study proposed the metaheuristic-based possibilistic multivariate fuzzy weighted c-means (PMFWCM) algorithm for clustering mixed data PMFWCM algorithm itself is normally used for numerical data. To implement in the real application of market segmentation, where the data usually contains both numerical and categorical attributes model improvement is a need. First, the distance between two mixed-attribute objects is calculated by using the object–cluster similarity measure. Then, three metaheuristics, i.e., genetic algorithm (GA), particle swarm optimization algorithm (PSO), and sine cosine algorithm (SCA), are employed to integrate with the PMFWCM algorithm for cluster analysis. This combination aims to improve the clustering performance of the PMFWCM algorithm and to make the clustering results more stable. To cluster a real-world dataset certainly, the experiment with benchmark datasets from UCI machine learning repository is conducted to verify the performance of the proposed algorithms. The experiment results show that the clustering performance of the SCA-PMFWCM, GA-PMFWCM and PSO-PMFWCM algorithms are better than that of the PMFWCM algorithm. Moreover, from case study results, the SCA-PMFWCM algorithm gives the smallest sum of squared error and computational time compared with the GA-PMFWCM, PSO-PMFWCM, and PMFWCM algorithms.

A PARTITION-BASED FEATURE SELECTION METHOD FOR MIXED DATA: A FILTER APPROACH

Feature selection is fundamentally an optimization problem for selecting relevant features from several alternatives in clustering problems. Though several algorithms have been suggested, however till this day, there has not been any one of those that has been dubbed as the best for every problem scenario. Therefore, researchers continue to strive in developing superior algorithms. Even though clustering process is considered a pre-processing task but what it really does is just dividing the data into groups. In this paper we have attempted an improved distance function to cluster mixed data. A similarity measure for mixed data is Gower distance is adopted and modified to define the similarity between object pairs. A partitional algorithm for mixed data is employed to group similar objects in clusters. The performance of the proposed method has been evaluated on similar mixed and real educational dataset in terms of the silhouette coefficient. Results reveal the effectiveness of this algorithm in unsupervised discovery problems. The proposed algorithm performed better than other clustering algorithms for various datasets.

Clustering mixed data using neighbourhood rough sets

Clustering mixed data using neighbourhood rough sets

Experiments on Clustering Algorithms for Mixed and Incomplete Data

Clustering mixed and incomplete data is a goal of frequent approaches in the last years because its common apparition in soft sciences problems. However, there is a lack of studies evaluating the performance of clustering algorithms for such kind of data. In this paper we present an experimental study about performance of seven clustering algorithms which used one of these techniques: partition, hierarchal or metaheuristic. All the methods ran over 15 databases from UCI Machine Learning Repository, having mixed and incomplete data descriptions. In external cluster validation using the indices Entropy and V-Measure, the algorithms that use the last technique showed the best results. Thus, we recommend metaheuristic based clustering algorithms for clustering data having mixed and incomplete descriptions.

Clustering mixed data using neighbourhood rough sets

Data in varied nature and huge quantities are being generated every day. They range from tabulated, structured and semi-structured as well as numerical or categorical in terms of attributes. Data pre-processing presents data in a favourable format to apply analytics algorithm and derive knowledge therein. Data analytics has revolutionised millennial mankind unwinding the knowledge and patterns mined from data. Clustering is an unsupervised learning pattern which has popular algorithms based on distance, density, dimensions and other functions. These algorithms are operational on numerical attributes and special algorithms for data involving categorical features are also reported. In this paper we propose a straight forward way of clustering data involving both numerical and categorical features based on neighbourhood rough sets. It does not include calculation of any extra parameters like entropy, saliency, dependency or call for discretisation of data. Hence its complexity is lesser than algorithms proposed for categorical or mixed data and offers better efficiency.

Notice of Retraction Dynamic clustering of data with modified K-prototype algorithm

Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of APTIKOM's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.The presenting author of this paper has the option to appeal this decision by contacting ij.aptikom@gmail.com.-----------------------------------------------------------------------Clustering of mixed numerical and categorical data has become a challenge now a days. A number of algorithms dealing with mixed data have been proposed. Speed and simplicity are the two major features that have made the K-Prototype algorithm a famous partition based clustering algorithm. This algorithm has a constraint of providing the value of K initially and sometimes predicting the optimum number of clusters in advance becomes practically impossible. In this paper, a new algorithm based on the K-Prototype algorithm for clustering mixed data with advanced features for automatic generation of appropriate number of clusters is presented.

Clustering mixed numeric and categorical data with artificial bee colony strategy

Clustering mixed numeric and categorical data with artificial bee colony strategy

Mixtures of general location model with factor analyzer covariance structure for clustering mixed type data

Cluster analysis is one of the most widely used method in statistical analyses, in which homogeneous subgroups are identified in a heterogeneous population. Due to the existence of the continuous and discrete mixed data in many applications, so far, some ordinary clustering methods such as, hierarchical methods, k-means and model-based methods have been extended for analysis of mixed data. However, in the available model-based clustering methods, by increasing the number of continuous variables, the number of parameters increases and identifying as well as fitting an appropriate model may be difficult. In this paper, to reduce the number of the parameters, for the model-based clustering mixed data of continuous (normal) and nominal data, a set of parsimonious models is introduced. Models in this set are extended, using the general location model approach, for modeling distribution of mixed variables and applying factor analyzer structure for covariance matrices. The ECM algorithm is used for estimating the parameters of these models. In order to show the performance of the proposed models for clustering, results from some simulation studies and analyzing two real data sets are presented.

Clustering Mixed Data Based on Density Peaks and Stacked Denoising Autoencoders

With the universal existence of mixed data with numerical and categorical attributes in real world, a variety of clustering algorithms have been developed to discover the potential information hidden in mixed data. Most existing clustering algorithms often compute the distances or similarities between data objects based on original data, which may cause the instability of clustering results because of noise. In this paper, a clustering framework is proposed to explore the grouping structure of the mixed data. First, the transformed categorical attributes by one-hot encoding technique and normalized numerical attributes are input to a stacked denoising autoencoders to learn the internal feature representations. Secondly, based on these feature representations, all the distances between data objects in feature space can be calculated and the local density and relative distance of each data object can be also computed. Thirdly, the density peaks clustering algorithm is improved and employed to allocate all the data objects into different clusters. Finally, experiments conducted on some UCI datasets have demonstrated that our proposed algorithm for clustering mixed data outperforms three baseline algorithms in terms of the clustering accuracy and the rand index.

Survey of State-of-the-Art Mixed Data Clustering Algorithms

Mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing. Clustering is often applied to mixed datasets to find structures and to group similar objects for further analysis. However, clustering mixed data is challenging because it is difficult to directly apply mathematical operations, such as summation or averaging, to the feature values of these datasets. In this paper, we present a taxonomy for the study of mixed data clustering algorithms by identifying five major research themes. We then present a state-of-the-art review of the research works within each research theme. We analyze the strengths and weaknesses of these methods with pointers for future research directions. Lastly, we present an in-depth analysis of the overall challenges in this field, highlight open research questions and discuss guidelines to make progress in the field.

An Optimizing and Differentially Private Clustering Algorithm for Mixed Data in SDN-Based Smart Grid

Software-defined network (SDN) is widely used in smart grid for monitoring and managing the communication network. Big data analytics for SDN-based smart grid has got increasing attention. It is a promising approach to use machine learning technologies to analyze a large amount of data generated in SDN-based smart grid. However, the disclosure of personal privacy information must receive considerable attention. For instance, data clustering in user electricity behavior analysis may lead to the disclosure of personal privacy information. In this paper, an optimizing and differentially private clustering algorithm named ODPCA is proposed. In the ODPCA, the differentially private K-means algorithm and K-modes algorithm are combined to cluster mixed data in a privacy-preserving manner. The allocation of privacy budgets is optimized to improve the accuracy of clustering results. Specifically, the loss function that considers both the numerical and categorical attributes between true centroids and noisy centroids is analyzed to optimize the allocation the privacy budget; the number of iterations of clustering is set to a fixed value based on the total privacy budget and the minimal privacy budget allocated to each iteration. It is proved that the ODPCA can meet the differential privacy requirements and has better performance by comparing with other popular algorithms.

Model-based clustering of Gaussian copulas for mixed data

ABSTRACTClustering of mixed data is important yet challenging due to a shortage of conventional distributions for such data. In this article, we propose a mixture model of Gaussian copulas for clustering mixed data. Indeed copulas, and Gaussian copulas in particular, are powerful tools for easily modeling the distribution of multivariate variables. This model clusters data sets with continuous, integer, and ordinal variables (all having a cumulative distribution function) by considering the intra-component dependencies in a similar way to the Gaussian mixture. Indeed, each component of the Gaussian copula mixture produces a correlation coefficient for each pair of variables and its univariate margins follow standard distributions (Gaussian, Poisson, and ordered multinomial) depending on the nature of the variable (continuous, integer, or ordinal). As an interesting by-product, this model generalizes many well-known approaches and provides tools for visualization based on its parameters. The Bayesian inference is achieved with a Metropolis-within-Gibbs sampler. The numerical experiments, on simulated and real data, illustrate the benefits of the proposed model: flexible and meaningful parameterization combined with visualization features.