Optimization of the Numeric and Categorical Attribute Weights in KAMILA Mixed Data Clustering Algorithm

Data mining discloses hidden, previously unknown, and potentially useful information from large amounts of data. As comparison to the traditional statistical and machine learning data analysis techniques, data mining emphasizes to provide a convenient and complete environment for the data analysis. Data mining has become a popular technology in analyzing complex data. Clustering is one of the data mining core techniques. In the field of data mining and data clustering, it is a highly desirable task to perform cluster analysis on large data sets with mixed numeric, categorical, ordinal, and ratio-scaled with binary and nominal values. However, most already available data merging and grouping through clustering algorithms are effective for the numeric data rather than the mixed data set. For this purpose, this paper makes efforts to present a new amalgamation algorithm for these mixed data sets by modifying the common cost function, trace of the within cluster dispersion matrix. The genetic algorithm (GA) is used to optimize the new cost function to obtain valid clustering result. We can compare and analyze that the GA-based clustering algorithm is feasible for the high-dimensional data sets with mixed data values that are obtained in real life results. Core Idea of Our Paper: By this paper, we try to describe a technique for estimating the cost function metrics from mixed numeric, categorical and other type databases by using an uncertain grade-of-membership clustering model with the efficiency of Genetic Algorithm. This technique can be applied to the problem of opportunity analysis for business decision-making. This general approach could be adapted to many other applications where a decision agent needs to assess the value of items from a set of opportunities with respect to a reference set representing its business. For processing numeric attributes, instead of generalizing them, a prototype may be developed for experiments with synthetic and real data sets, and comparison with those of the traditional approaches. The results confirmed the feasibility of the framework and the superiority of the extended techniques.

In the real-world applications, heterogeneous interdependent attributes that consist of both discrete and numerical variables can be observed ubiquitously. The usual representation of these data sets is an information table, assuming the independence of attributes. However, very often, they are actually interdependent on one another, either explicitly or implicitly. Limited research has been conducted in analyzing such attribute interactions, which causes the analysis results to be more local than global. This paper proposes the coupled heterogeneous attribute analysis to capture the interdependence among mixed data by addressing coupling context and coupling weights in unsupervised learning. Such global couplings integrate the interactions within discrete attributes, within numerical attributes and across them to form the coupled representation for mixed type objects based on dimension conversion and feature selection. This work makes one step forward towards explicitly modeling the interdependence of heterogeneous attributes among mixed data, verified by the applications in data structure analysis, data clustering evaluation, and density comparison. Substantial experiments on 12 UCI data sets show that our approach can effectively capture the global couplings of heterogeneous attributes and outperforms the state-of-the-art methods, supported by statistical analysis.

Clustering algorithms play a pivotal role in data mining, offering powerful tools for uncovering hidden patterns and structures within datasets. These algorithms aim to divide data points into coherent groups based on similarities or dissimilarities, making it easier to explore and understand complex data. Clustering algorithms typically rely on similarity measures to assess the likeness between data points. Consequently, selecting a suitable similarity measure is crucial for achieving satisfactory clustering outcomes. However, this decision can pose significant challenges, especially for non-experts, given the plethora of similarity measures available in the literature and their performance which is closely linked to the specific dataset, clustering algorithm, and cluster validity index employed. This difficulty is even more important when considering mixed data clustering. Mixed data refers to heterogeneous data characterized by both numerical and categorical attributes. In such a context, the same similarity measure cannot be used for both types of attributes due to their different nature. Commonly, two similarity measures are combined, one for numerical attributes and one for categorical attributes. This adds a layer of complexity to the problem since it requires the selection of two similarity measures instead of just one. This paper introduces SIMREC, a similarity measure recommendation system for mixed data clustering. The system uses meta-learning to mine the relationship between dataset characteristics and similarity measures performances for different mixed data clustering algorithms and cluster validity indices. Therefore, given a mixed dataset, a mixed data clustering algorithm, and a cluster validity index, the system can recommend suitable pairs of numerical and categorical similarity measures based on the characteristics of the dataset. We implemented the proposed system using 130 pairs of similarity measures (10 numerical and 13 categorical), 4 commonly used mixed data clustering algorithms (K-Prototypes, LSH-K-Prototypes, K-Medoids, and Hierarchical Clustering), and three cluster validity indices (Silhouette, Clustering Accuracy, and Adjusted Rand Index). Our experiments on 185 publicly available mixed datasets show that the pairs of similarity measures recommended by SIMREC outperform the baseline pairs, including classically used pairs of similarity measures in the literature.

Many scientific works on grouping mixed data have chosen different experimental scenarios to structure their findings, which involve deciding from the programming language to the use of real-world and/or simulated datasets and performance measures, for example. Due to these characteristics directly influence the conclusion of the studies and the way new scientific works are proposed, it would be useful to have a wide map with the main choices that have been done by the authors of high-impact scientific documents so that the community can reflect on the thematic direction, identify best practices, and propose new paths for future research. To the best of our knowledge, such a map does not exist, neither a methodological procedure to build it. Therefore, this paper proposes a systematic methodology to reach such maps and provides a wide and in-depth map of the main choices the authors of high-impact scientific documents on mixed data clustering and surrounding studies have done in their experiments. As a result, 160 documents were systematically selected and classified into one of the six class of data clustering approaches, besides individually tabulated. From the tables for each class, we found, for instance, that real-world datasets are used more frequently than simulated ones, the documents used more external indices, followed by internal and relative ones, it is not common for the authors to inform the programming language they have used, except in the partitional class. We also provided the address of the algorithms’ code when they are made available by the authors.

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.

Task scheduling optimization in heterogeneous cloud computing environments: A hybrid GA-GWO approach

Aimed for the problems in traditional genetic algorithm of low search efficiency and easily falling into the local optimal solution, an improved genetic algorithm is proposed in this paper. So as to save the storage space, the simple one-dimensional code method is adopted to replace the method of complex two-dimensional coding. In the design of genetic operators, many operations such as crossover and mutation are redefined to avoid getting into the local optimum. Then the two fitness functions of collision-free path and the shortest distance are fused into one for the following genetic optimization. In the case of the same population parameters, 100 trials are respectively developed with the method of improved genetic algorithm and traditional genetic algorithm. Among them, the improved genetic algorithm to search the optimal path gets to 95 times, and the shortest path is 20.9706. Besides, the average searching time takes up 217ms. While the number of traditional method to search for the optimal path reaches up to 62 times, the shortest path can be 25.0711, and the average searching time needs 345ms. So compared to the tests results referred above, the improved genetic algorithm is more efficient and can get a better solution than the traditional genetic algorithm.

This paper presents a crowding niche cellular genetic algorithm (referred to NCGA) aiming at solving the problems of local convergence and non-uniform population distribution in traditional genetic algorithm. The selecting operation in traditional genetic algorithm is improved by bringing in the concept of neighbors of cellular genetic algorithm, and the population distribution is greatly enhanced by introducing crowding niche mechanism, which betters the ability of global searching and helps to avoid the population local convergence. Meanwhile, the paper describes the crowding niche cellular genetic algorithm in details and compares it with simple genetic algorithm （SGA） and simple niche genetic algorithm （NGA）; the comparison results reveal that, NCGA outperforms the other two algorithms in terms of convergence rate and population diversity.

To find out if the use of different virtual monoenergetic data sets enabled by DECT technology might have a negative impact on post-processing applications, specifically in case of the "unfolded ribs" algorithm. Metal or beam hardening artifacts are suspected to generate image artifacts and thus reduce diagnostic accuracy. This paper tries to find out how the generation of "unfolded rib" CT image reformates is influenced by different virtual monoenergetic CT images and looks for possible improvement of the post-processing tool. Between March 2021 and April 2021, thin-slice dual-energy CT image data of the chest were used creating "unfolded rib" reformates. The same data sets were analyzed in three steps: first the gold standard with the original algorithm on mixed image data sets followed by the original algorithm on different keV levels (40-120keV) and finally using a modified algorithm which in the first step used segmentation based on mixed image data sets, followed by segmentation based on different keV levels. Image quality (presence of artifacts), lesion and fracture detectability were assessed for all series. Both, the original and the modified algorithm resulted in more artifact-free image data sets compared to the gold standard. The modified algorithm resulted in significantly more artifact-free image data sets at the keV-edges (40-120keV) compared the original algorithm. Especially "black artifacts" and pseudo-lesions, potentially inducing false positive findings, could be reduced in all keV level with the modified algorithm. Detection of focal sclerotic, lytic or mixed (k = 0.990-1.000) lesions was very good for all keV levels. The Fleiss-kappa test for detection of fresh and old rib fractures was ≥ 0.997. The use of different virtual monoenergetic keVs for the "unfolded rib" algorithm is generating different artifacts. Segmentation-based artifacts could be eliminated by the proposed new algorithm, showing the best results at 70-80keV.

The process of Medical image registration is essentially to find a optimal transformation between two medical images, Because of the advantages and disadvantages of various optimization algorithm is relative, this paper proposed by the simplex algorithm and genetic algorithm hybrid optimization algorithm combining for registration parameter optimization, analysed and compared with the traditional genetic algorithm through experiment, verify the simplex genetic algorithm whether in registration precision or in registration efficiency are obviously better than the traditional genetic algorithm, Is a kind of practical optimization algorithm.

The main problem on community detection using traditional genetic algorithms (GA) lies in the slow speed of convergence. This paper attempts to apply parallel genetic algorithms (PGA) to explore community structure in complex networks in order to improve the efficiency of traditional genetic algorithms. Several different designing ways of PGA are discussed and compared. Experimental results based on the GN benchmark networks, LFR benchmark networks, and eight real-world networks, confirm the PGA with coarse-grained-master-slave hybrid model spends less time yet achieves higher accuracy than traditional genetic algorithms.

This paper proposes a Social Genetic Algorithm (SGA) that includes a transformation function that has ability to improve search efficiency. The SGA is different from the Traditional Genetic Algorithm (TGA) approaches, as it allows refinement of the TGA parameters for the selections of operators in each generation with two functions: optimization of crossover rate and optimization of mutation rate. In this paper, a new function that optimizes gene relationship has been introduced to advance the evolution capability and flexibility of SGA in searching complex and large solution space. Our proposed approach has been evaluated using simulation models. The simulation results have shown that SGA outperforms TGA in improving search efficiency. The contribution of the proposed approach is a dynamic and adaptive methodology, which has ability to improve efficiency.

When the traditional genetic algorithm was used to solve the community detection problem, it was not easy to avoid the problems of low efficiency and slow convergent speed. To be aim at these problems, a improved genetic algorithm which is based on the immune mechanism was proposed in this paper. In this new algorithm, the immune mechanism was used to ensure the diversity of population. Meanwhile, a improved character encoding was adopted to further reduce the search space. The results shows that the shortcomings of slow convergent speed and low efficiency could be overcome by using the improved genetic algorithm to solve these problems, compared with the traditional genetic algorithm.

This article introduces a novel clustering algorithm for dealing with mixed and incomplete data descriptions of instances: the A2FCPAntSA algorithm. Unlike previous proposals, this new algorithm uses groups of instances as initial clusters in an agglomerative clustering scheme. It also merges in a single step, all of the most similar clusters. In addition, the proposal incorporates the use of bio-inspired algorithms to refine the obtained clusters. The numerical experiments carried out over several repository datasets display the superiority of the proposal with respect to other state of the art clustering algorithms, for mixed and incomplete data, by considering several dissimilarity measures and supervised cluster validity indexes.

A common and old problem in statistics is the separation of a heterogeneous population into more homogeneous subpopulations. A wide variety of approaches and techniques for tackling this problem now exist. The finite mixture model is one such technique used to analyse a given data set and create natural groups. The main advantage of mixture models is that they offer a very flexible and relatively easy method of fitting to a data set.The main focus of this thesis will be the clustering of data with the aid of mixed variable mixture models. There has been a lot of research and many papers written with regard to the analysis of continuous variables. This thesis, however, will be concerned with the techniques that will be able to analyse data from continuous to mixed variables, a combination of continuous and discrete variables.The expectation-maximization (EM) algorithm of Dempster et al. (1977) will be used in this thesis for the clustering of mixed variable data. As demonstrated effectively in numerous publications, this algorithm solves estimation of the model parameters iteratively under an advantageous property of monotonic convergence. An extension of the EM algorithm, the ECM algorithm introduced by Meng and Rubin (1993), will also be considered in this thesis for itsrole within more complicated mixed mixture models.On the application of mixed mixture models, this thesis will use a joint distribution specified by the conditional distribution of the continuous variables, given the values of the discrete variables times the marginal distribution of the latter. There are three models - naive, logistic and multinomial - considered for the discrete variables and two models - independent and location - considered for the continuous variables. These models are combined to make a total of sixmodels for the analysis of mixed variable data.The program EMM, its design and development will be discussed in this thesis. Many real mixed data sets and some simulations will also be analysed to investigate how these proposed methods will compare with existing methods. Some of these data sets will be transformed so as to create a suitable input for the program and hence provide a comparable output so that comparisons can be made.The program EMM is written in the FORTRAN computer language. This means that it is well organised with each segment arranged in separate sub-routines. This allows for easy modification to any part of the program or alternatively the addition of a new subroutine so as to increase the capabilities of the model.