Clustering is a commonly used method for exploring and analysing data where the primary objective is to categorise observations into similar clusters. In recent decades, several algorithms and methods have been developed for analysing clustered data. We notice that most of these techniques deterministically define a cluster based on the value of the attributes, distance, and density of homogenous and single-featured datasets. However, these definitions are not successful in adding clear semantic meaning to the clusters produced. Evolutionary operators and statistical and multidisciplinary techniques may help in generating meaningful clusters. Based on this premise, we propose a new evolutionary clustering algorithm (ECA*) based on social class ranking and meta-heuristic algorithms for stochastically analysing heterogeneous and multifeatured datasets. The ECA* is integrated with recombinational evolutionary operators, Levy flight optimisation, and some statistical techniques, such as quartiles and percentiles, as well as the Euclidean distance of the K-means algorithm. Experiments are conducted to evaluate the ECA* against five conventional approaches: K-means (KM), K-means++ (KM++), expectation maximisation (EM), learning vector quantisation (LVQ), and the genetic algorithm for clustering++ (GENCLUST++). That the end, 32 heterogeneous and multifeatured datasets are used to examine their performance using internal and external and basic statistical performance clustering measures and to measure how their performance is sensitive to five features of these datasets (cluster overlap, the number of clusters, cluster dimensionality, the cluster structure, and the cluster shape) in the form of an operational framework. The results indicate that the ECA* surpasses its counterpart techniques in terms of the ability to find the right clusters. Significantly, compared to its counterpart techniques, the ECA* is less sensitive to the five properties of the datasets mentioned above. Thus, the order of overall performance of these algorithms, from best performing to worst performing, is the ECA*, EM, KM++, KM, LVQ, and the GENCLUST++. Meanwhile, the overall performance rank of the ECA* is 1.1 (where the rank of 1 represents the best performing algorithm and the rank of 6 refers to the worst performing algorithm) for 32 datasets based on the five dataset features mentioned above.

A multidisciplinary ensemble algorithm for clustering heterogeneous datasets

In recent years, modern appliances with high electricity demand have played a significant role in residential energy consumption. Despite the positive impact of these appliances on the quality of life, they suffer from major drawbacks, such as serious environmental concerns and high electricity bills. This paper introduces a consolidated framework of load management to alleviate those drawbacks. Initially, benefiting from a demonstrative analysis of home energy consumption data, controllable and responsive appliances in smart home are identified. Then, the energy consumption pattern is reduced and shifted using flexible load models and better utilization of existing energy storage systems. This can be achieved through data mining approaches, i.e., density-based spatial clustering of application with noise (DBSCAN) method. In this technique, no sensor for detection or measurement instruments will be required, whose deployment incur cost to the system or increase security risk for consumers. In the following, one scheduling of using controllable appliances, which is formulated by convex optimization, is considered for the demand response (DR) program, provided that this plan doesn't affect customers’ priority and convenience. In the last stage, the deployment of energy storage systems, such as plug-in hybrid electric vehicles (PHEVs) and battery energy storage systems (BESS), is introduced to lower the energy cost and improve the performance of the proposed DR model. Simulation results of this demand response are compared with conventional k-clustering methods to confirm the economic superiority of the DBSCAN clustering technique using the data of a residential unit during three different scenarios.

A data-mining based optimal demand response program for smart home with energy storages and electric vehicles

A fuzzy clustering algorithm for developing predictive models in construction applications

The performance of construction systems (e.g., activities, operations, projects) is commonly measured using different indicators, such as productivity or production rate. The accurate prediction of performance, which is an important concern of construction researchers and practitioners, requires effective techniques for construction modeling. However, the complexity of construction systems creates three challenges for construction modeling: (1) construction systems are affected by numerous interacting factors, (2) the factors that affect construction systems often exhibit both probabilistic and non-probabilistic uncertainty, and (3) construction systems are dynamic. Fuzzy system dynamics (FSD) is a simulation technique that can be used for modeling construction systems with the potential to address these three challenges. However, the application of FSD in construction is still limited due to its low accuracy for modeling the non-linear, complex, and highly-dimensional relationships that exist between the system variables. Currently, these system relationships are most often defined in FSD by linear regression, due its computational simplicity. This paper introduces a new hybrid technique — neuro-fuzzy system dynamics (N-FSD) — by integrating FSD with hybrid neuro-fuzzy systems. In N-FSD, hybrid neuro-fuzzy systems are used to define the non-linear, complex and high-dimensional relationships between the system variables, which improves the accuracy of FSD models in construction applications. The applicability of the N-FSD technique is tested through a construction case study by modeling the production rate of earthmoving operations.

Neuro-fuzzy system dynamics technique for modeling construction systems

Acoustic emission is a nondestructive control technique as it does not involve any input of energy into the materials. It is based on the acquisition of ultrasonic signals spontaneously emitted by a material under stress due to irreversible phenomena such as damage, microcracking, degradation, and corrosion. It is a dynamic and passive-receptive technique that analyzes the ultrasonic pulses emitted by a crack when it is generated. This technique allows for an early diagnosis of incipient structural damage by capturing the precursor signals of the fracture. Recently, the scientific community is making extensive use of methodologies based on machine learning: the use of machine learning makes a machine capable of receiving a series of data, modifying the algorithms as they receive information on what they are processing. In this way, the machine can learn without being explicitly programmed, and this implies a huge use of data and an efficient algorithm to adapt. This review described the methodologies for the implementation of the acoustic emission (AE) technique in the evaluation of the conditions and in the monitoring of materials and structures. The latest research products were also analyzed in the development of new methodologies based on machine learning for the detection and localization of damage for the characterization of the fracture and the prediction of the failure mode. The work carried out highlighted the strong use of these methods, which confirms the extreme usefulness of these techniques in identifying structural damage in scenarios heavily contaminated by residual noise.

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Machine-Learning-Based Methods for Acoustic Emission Testing: A Review

In modern world, we have to deal with huge volumes of data which include image, video, text and web documents, DNA, microarray gene data, etc. Organizing such data into rational groups is a critical first step to draw inferences. Data clustering analysis has emerged as an effective technique to accurately accomplish the task of categorizing data into sensible groups. Clustering has a rich association with researches in various scientific domains. One of the most popular clustering algorithms, k-means algorithm was proposed as early as 1957. Since then, many clustering algorithms have been developed and used, to group data in various commercial and non-commercial sectors alike. In this paper, we have given concise description of the existing types of clustering approaches followed by a survey of the fields where clustering analytics has been effectively employed in pattern recognition and knowledge discovery.

A Short Review on Different Clustering Techniques and Their Applications

Feature selection is one of the important preprocessing steps in machine learning and data mining domain However, finding the best feature subsets for large datasets is computationally expensive task Meanwhile, quantum computing has emerged as a new computational model that is able to speed up many classical computationally expensive problems Annealing-based quantum model, for example, finds the lowest energy state of an Ising model Hamiltonian, which is the formalism for Quadratic Unconstrained Binary Optimization (QUBO) Due to its capabilities in producing quality solution to the hard combinatorial optimization problems with less computational effort, quantum annealing has the potentiality in feature subset selection Although several hard optimization problems are solved, using quantum annealing, not sufficient work has been done on quantum annealing based feature subset selection Though the reported approaches have good theoretical foundation, they usually lack required empirical rigor In this paper, we attempt to reduce classical benchmark feature evaluation functions like mRMR, JMI, and FCBF to QUBO formulation, enabling the use of quantum annealing based optimization to feature selection We then apply QUBO of ten datasets using both Simulated Annealing (SA) and Simulated Quantum Annealing (SQA) and compared the result Our empirical results confirm that, for seven in ten datasets, SQA is able to produce at most equal or less number of features in all selected subset compared to SA does SQA also results in stable feature subsets for all datasets

An Approach of Feature Subset Selection Using Simulated Quantum Annealing

Natural language processing (NLP) is a booming field in this era of data, where almost all businesses and organizations have access to many review sites, social media, and e-commerce websites. Recently, deep learning models have shown state-of-the-art results in NLP tasks. With the help of complex models like long-short term memory, various problems such as vanishing gradient problem have been diminished and new models like the attention model or aspect embedding increases accuracy. These made a drastic change in the field of sentiment analysis and made it more business-oriented, like most of the big business organizations, for example, Amazon and Flipkart, where it is used for analyzing details about their customer review. Some researchers have shown us a way to not even using complex models like LSTM we can do so, even better with adding gating mechanism to our well-known CNN. Watching all of these, we are going to do a brief review of many technologies discovered by many scientists across the world and focus on some of the state-of-the-art works done in the domain of sentiment analysis.

A Survey on Sentiment Analysis

In this article, a graph-theoretic method for supervised feature selection using matrix exponential of pairwise correlation value has been illustrated. In machine learning, high-dimensional data sets have a enormous number of redundant and irrelevant features. The sum of mean and standard deviation of exponential matrix has been set as the threshold for selecting relevant features. Principles of vertex cover and independent set have then been used to remove redundant features. In the next step, mutual information value has been used to select relevant features that were initially rejected. The results show that this method has performed better than the benchmark algorithms when experimented on multiple standard data sets.

Mrityunjoy Panday

Papers

A Short Review on Different Clustering Techniques and Their Applications

An Approach of Feature Subset Selection Using Simulated Quantum Annealing

A Survey on Sentiment Analysis

Graph-Based Supervised Feature Selection Using Correlation Exponential