An Affinity Propagation Clustering Algorithm for Mixed Numeric and Categorical Datasets

Clustering has been widely used in different fields of science, technology, social science, and so forth. In real world, numeric as well as categorical features are usually used to describe the data objects. Accordingly, many clustering methods can process datasets that are either numeric or categorical. Recently, algorithms that can handle the mixed data clustering problems have been developed. Affinity propagation (AP) algorithm is an exemplar-based clustering method which has demonstrated good performance on a wide variety of datasets. However, it has limitations on processing mixed datasets. In this paper, we propose a novel similarity measure for mixed type datasets and an adaptive AP clustering algorithm is proposed to cluster the mixed datasets. Several real world datasets are studied to evaluate the performance of the proposed algorithm. Comparisons with other clustering algorithms demonstrate that the proposed method works well not only on mixed datasets but also on pure numeric and categorical datasets

A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach. In this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome. Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin

Clustering Data of Mixed Categorical and Numerical Type with Unsupervised Feature Learning

Mixed-type categorical and numerical data are a challenge in many applications. This general area of mixed-type data is among the frontier areas, where computational intelligence approaches are often brittle compared with the capabilities of living creatures. In this paper, unsupervised feature learning (UFL) is applied to the mixed-type data to achieve a sparse representation, which makes it easier for clustering algorithms to separate the data. Unlike other UFL methods that work with homogeneous data, such as image and video data, the presented UFL works with the mixed-type data using fuzzy adaptive resonance theory (ART). UFL with fuzzy ART (UFLA) obtains a better clustering result by removing the differences in treating categorical and numeric features. The advantages of doing this are demonstrated with several real-world data sets with ground truth, including heart disease, teaching assistant evaluation, and credit approval. The approach is also demonstrated on noisy, mixed-type petroleum industry data. UFLA is compared with several alternative methods. To the best of our knowledge, this is the first time UFL has been extended to accomplish the fusion of mixed data types

Algorithm K-Prototype to Clustering The Earthquake on Sulawesi Island

Natural disasters that had occurred in Indonesia consist of hydro-meteorology: floods, droughts, and landslides, geophysical: volcanic earthquakes and volcanic eruptions, and biological: epidemics. Regarding the tectonic earthquake on Sulawesi Island, there are at least 2 earthquake disasters that became national disasters, namely in Central Sulawesi and West Sulawesi in the range of 2017 to 2021. This study aims to cluster tectonic earthquakes on Sulawesi Island, from 2017 to 2020, as the basis for formulating disaster mitigation plans. This study used tectonic earthquake data from 2017 to 2020 obtained from BMKG Gowa, Indonesia. The variables used are magnitude, depth, and distance category. Because they are mixed variables, this study used a k-prototype algorithm. There are four clusters in 2017, six clusters in 2018, five clusters in 2019, and six clusters in 2020 based on the ratio of within-cluster distance against between-cluster distance. It can be related to the active fault on Sulawesi Island. The characteristics of clusters form each year are the greater magnitude of the earthquake, the deeper of deep and the category distance is dominated by the regional level

Empirical Comparative Analysis of 1-of-K Coding and K-Prototypes in Categorical Clustering

Clustering is a fundamental machine learning application, which partitions data into homogeneous groups. K-means and its variants are the most widely used class of clustering algorithms today. However, the original k-means algorithm can only be applied to numeric data. For categorical data, the data has to be converted into numeric data through 1-of-K coding which itself causes many problems. K-prototypes, another clustering algorithm that originates from the k-means algorithm, can handle categorical data by adopting a different notion of distance. In this paper, we systematically compare these two methods through an experimental analysis. Our analysis shows that K-prototypes is more suited when the dataset is large-scaled, while the performance of k-means with 1-of-K coding is more stable. We believe these are useful heuristics for clustering methods working with highly categorical data