Exploratory Data Mining Research Articles

An Overview of Characteristics and Application of Big Data

Big data is huge volume, heterogeneous, appropriated data. Big data applications where data gathering has developed ceaselessly, it is costly to oversee, catch or concentrate and procedure data utilizing existing programming apparatuses. Clustering Technique for unstructured for big data is a primary errand of exploratory data investigation and data mining applications. This phenomenon, commonly referred to as Big Data, presents both opportunities and challenges across various domains. This paper provides a comprehensive overview of the characteristics and applications of Big Data. it delves into the characteristics of Big Data, including its volume, velocity, variety, veracity, and value. Understanding these characteristics is crucial for harnessing the full potential of Big Data analytics. the paper explores the diverse applications of Big Data across industries such as healthcare, finance, retail, manufacturing, and transportation.

Customer satisfaction antecedents in uncertain hospitality conditions: an exploratory data mining approach

PurposeInformed by trait and self-determination theories, the present study aims to extend the knowledge regarding the link between customer satisfaction (CS) and its antecedents, including job autonomy (JA), conscientiousness, customer uncertainty (CU) and extra-role customer service (E-RCS) in the hospitality industry.Design/methodology/approachA total of 306 frontline employees were selected from the hotels in North Cyprus, Turkey. Psychometric properties, including the validity and reliability of study variables, were assessed in the first step using confirmatory factor analysis. Then, the data were analyzed utilizing machine learning methods, mainly three exploratory data mining techniques, including lasso regression, decision trees and random forest, as well as partial dependence plots to visualize the role of suggested predictors on the outcome variable.FindingsData mining analysis shows that employees who can modify their job objectives are better equipped to satisfy customers in uncertain situations (JA8). In addition, the findings reveal that employees who believe they work hard to accomplish their personal and organizational goals (CON7) while also having the freedom to decide how to approach their job (JA1) and choose the procedures to utilize (JA2) are more likely to contribute to CS. In general, CS peaked when JA was high, but conscientiousness was moderate, while CU was low.Practical implicationsThis study bridges the gap among various factors at the employee and customer individual, corporate and macro-environmental levels. Hospitality organizations can cultivate a culture of autonomy and independence by promoting open communication and offering growth and development opportunities. This approach enhances conscientious employees’ engagement, leading to exceptional customer service performance, particularly, in uncertain situations.Originality/valueFrom the methodology perspective, this work proposes an opportunity for prospective scientists to broaden the trait and self-determination theories research model by relying on the riches of exploratory techniques without the limits imposed by traditional analytical techniques. Further, this study advances the current knowledge about service agility under uncertainty by extending organizational and service management research to consumer behavior literature.

Identification of Genomic Signatures for Colorectal Cancer Survival Using Exploratory Data Mining.

Clinicopathological presentations are critical for establishing a postoperative treatment regimen in Colorectal Cancer (CRC), although the prognostic value is low in Stage 2 CRC. We implemented a novel exploratory algorithm based on artificial intelligence (explainable artificial intelligence, XAI) that integrates mutational and clinical features to identify genomic signatures by repurposing the FoundationOne Companion Diagnostic (F1CDx) assay. The training data set (n = 378) consisted of subjects with recurrent and non-recurrent Stage 2 or 3 CRC retrieved from TCGA. Genomic signatures were built for identifying subgroups in Stage 2 and 3 CRC patients according to recurrence using genomic parameters and further associations with the clinical presentation. The summarization of the top-performing genomic signatures resulted in a 32-gene genomic signature that could predict tumor recurrence in CRC Stage 2 patients with high precision. The genomic signature was further validated using an independent dataset (n = 149), resulting in high-precision prognosis (AUC: 0.952; PPV = 0.974; NPV = 0.923). We anticipate that our genomic signatures and NCCN guidelines will improve recurrence predictions in CRC molecular stratification.

Exploring data mining and machine learning in gynecologic oncology

Gynecologic (GYN) malignancies are gaining new and much-needed attention, perpetually fueling literature. Intra-/inter-tumor heterogeneity and “frightened” global distribution by race, ethnicity, and human development index, are pivotal clues to such ubiquitous interest. To advance “precision medicine” and downplay the heavy burden, data mining (DM) is timely in clinical GYN oncology. No consolidated work has been conducted to examine the depth and breadth of DM applicability as an adjunct to GYN oncology, emphasizing machine learning (ML)-based schemes. This systematic literature review (SLR) synthesizes evidence to fill knowledge gaps, flaws, and limitations. We report this SLR in compliance with Kitchenham and Charters’ guidelines. Defined research questions and PICO crafted a search string across five libraries: PubMed, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar—over the past decade. Of the 3499 potential records, 181 primary studies were eligible for in-depth analysis. A spike (60.53%) corollary to cervical neoplasms is denoted onward 2019, predominantly featuring empirical solution proposals drawn from cohorts. Medical records led (23.77%, 53 art.). DM-ML in use is primarily built on neural networks (127 art.), appoint classification (73.19%, 172 art.) and diagnoses (42%, 111 art.), all devoted to assessment. Summarized evidence is sufficient to guide and support the clinical utility of DM schemes in GYN oncology. Gaps persist, inculpating the interoperability of single-institute scrutiny. Cross-cohort generalizability is needed to establish evidence while avoiding outcome reporting bias to locally, site-specific trained models. This SLR is exempt from ethics approval as it entails published articles.

Information literacy of college students from library education in smart classrooms: based on big data exploring data mining patterns using Apriori algorithm

Information literacy of college students from library education in smart classrooms: based on big data exploring data mining patterns using Apriori algorithm

Exploring data mining tool in the management of Dementia with neuroprotective agent laden nano lipid carrier

Exploring data mining tool in the management of Dementia with neuroprotective agent laden nano lipid carrier

Methodological Basis and Experience of Using Data Mining Methods in Trade

The article explores data mining methods that allow us to get helpful information from the data. The possibility of using these methods in practice in the financial sector was considered. Since financial activity is closely related to our social life, the use of data mining methods plays an essential role in the analysis and forecasting of the financial market in the modern era of big data. However, due to differences in the experience of researchers in different disciplines, it is not easy to use data mining methods when analyzing financial data. Therefore, creating a methodological basis for the practical application of data mining methods in the analysis of financial data is an urgent issue. The purpose of this article is to create a methodological basis for using data mining methods for efficient trading. When processing product data, a priori methods and visualization methods were used, and their implementation in practice was described. As a result, scenarios of computer applications were created as a sample of the practical implementation of the algorithms of these methods. Building a quantitative trading strategy requires first statistical analysis of the information in the market and then testing the quantitative model on the collected data. This study developed a quantitative trading system based on data mining methods. The primary development tool used is the Jupyter web platform, and three cores have been developed: quantitative data selection, strategy testing on data, time series analysis, and visualization. The developed system supports modules for making simple trading decisions.

Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis

Mass-Suite (MSS) is a Python-based, open-source software package designed to analyze high-resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) data, particularly for water quality assessment and other environmental applications. MSS provides flexible, user-defined workflows for HRMS data processing and analysis, including both basic functions (e.g., feature extraction, data reduction, feature annotation, data visualization, and statistical analyses) and advanced exploratory data mining and predictive modeling capabilities that are not provided by currently available open-source software (e.g., unsupervised clustering analyses, a machine learning-based source tracking and apportionment tool). As a key advance, most core MSS functions are supported by machine learning algorithms (e.g., clustering algorithms and predictive modeling algorithms) to facilitate function accuracy and/or efficiency. MSS reliability was validated with mixed chemical standards of known composition, with 99.5% feature extraction accuracy and ~ 52% overlap of extracted features relative to other open-source software tools. Example user cases of laboratory data evaluation are provided to illustrate MSS functionalities and demonstrate reliability. MSS expands available HRMS data analysis workflows for water quality evaluation and environmental forensics, and is readily integrated with existing capabilities. As an open-source package, we anticipate further development of improved data analysis capabilities in collaboration with interested users.Graphical abstract

Identification of risk factors for adverse drug reactions in a pharmacovigilance database.

In addition to identifying new safety signals, pharmacovigilance databases could be used to identify potential risk factors for adverse drug reactions (ADRs). To evaluate whether data mining in a pharmacovigilance database can be used to identify known and possible novel risk factors for ADRs, for use in pharmacovigilance practice. Exploratory data mining was performed within the Swedish national database of spontaneously reported ADRs. Bleeding associated with direct oral anticoagulants (DOACs)-rivaroxaban, apixaban, edoxaban, and dabigatran-was used as a test model. We compared demographics, drug treatment, and clinical features between cases with bleeding (N = 965) and controls who had experienced other serious ADRs to DOACs (N = 511). Statistical analysis was performed by unadjusted and age adjusted logistic regression models, and the random forest based machine-learning method Boruta. In the logistic regression, 13 factors were significantly more common among cases of bleeding compared with controls. Eleven were labelled or previously proposed risk factors. Cardiac arrhythmia (e.g., atrial fibrillation), hypertension, mental impairment disorders (e.g., dementia), renal and urinary tract procedures, gastrointestinal ulceration and perforation, and interacting drugs remained significant after adjustment for age. In the Boruta analysis, high age, arrhythmia, hypertension, cardiac failure, thromboembolism, and pharmacodynamically interacting drugs had a larger than random association with the outcome. High age, cardiac arrhythmia, hypertension, cardiac failure, and pharmacodynamically interacting drugs had odds ratios for bleeding above one, while thromboembolism had an odds ratio below one. We demonstrated that data mining within a pharmacovigilance database identifies known risk factors for DOAC bleeding, and potential risk factors such as dementia and atrial fibrillation. We propose that the method could be used in pharmacovigilance for identification of potential ADR risk factors that merit further evaluation.

Closing the Affordable Housing Gap: Identifying the Barriers Hindering the Sustainable Design and Construction of Affordable Homes

Despite the commitment of the United Nations (UN) to provide everyone with equal access to basic services, the construction sector still fails to reach the production capacity and quality standards which are needed to meet the fast-growing demand for affordable homes. Whilst innovation measures are urgently needed to address the existing inefficiencies, the identification and development of the most appropriate solutions require a comprehensive understanding of the barriers obstructing the design and construction phase of affordable housing. To identify such barriers, an exploratory data mining analysis was conducted in which agglomerative hierarchical clustering made it possible to gather latent knowledge from 3566 text-based research outputs sourced from the Web of Science and Scopus. The analysis captured 83 supply-side barriers which impact the efficiency of the value chain for affordable housing provision. Of these barriers, 18 affected the design and construction phase, and after grouping them by thematic area, seven key matters of concern were identified: (1) design (not) for all, (2) homogeneity of provision, (3) unhealthy living environment, (4) inadequate construction project management, (5) environmental unsustainability, (6) placemaking, and (7) inadequate technical knowledge and skillsets. The insights which resulted from the analysis were seen to support evidence-informed decision making across the affordable housing sector. The findings suggest that fixing the inefficiencies of the affordable housing provision system will require UN Member States to accelerate the transition towards a fully sustainable design and construction process. This transition should prioritize a more inclusive and socially sensitive approach to the design and construction of affordable homes, capitalizing on the benefits of greater user involvement. In addition, transformative actions which seek to deliver more resource-efficient and environmentally friendly homes should be promoted, as well as new investments in the training and upskilling of construction professionals.

Retrospective content analysis of consumer product reviews related to chronic pain.

Chronic pain (CP) lasts for more than 3 months, causing prolonged physical and mental burdens to patients. According to the US Centers for Disease Control and Prevention, CP contributes to more than 500 billion US dollars yearly in direct medical cost plus the associated productivity loss. CP is complex in etiology and can occur anywhere in the body, making it difficult to treat and manage. There is a pressing need for research to better summarize the common health issues faced by consumers living with CP and their experience in accessing over-the-counter analgesics or therapeutic devices. Modern online shopping platforms offer a broad array of opportunities for the secondary use of consumer-generated data in CP research. In this study, we performed an exploratory data mining study that analyzed CP-related Amazon product reviews. Our descriptive analyses characterized the review language, the reviewed products, the representative topics, and the network of comorbidities mentioned in the reviews. The results indicated that most of the reviews were concise yet rich in terms of representing the various health issues faced by people with CP. Despite the noise in the online reviews, we see potential in leveraging the data to capture certain consumer-reported outcomes or to identify shortcomings of the available products.

Distributed fuzzy clustering algorithm for mixed-mode data in Apache SPARK

Fuzzy clustering is an invaluable data mining technique that allows each data point to belong to more than one cluster with some degree of membership. It is widely employed in exploratory data mining to discover overlapping communities in social networks, find structure in spectral data, and capture user interests in recommendation systems. Nowadays, the variety and volume of data are increasing at a tremendous rate. Data is power; the massive data, along with an effective technique, can unravel valuable information. The existing fuzzy clustering algorithms do not perform well on massive heterogeneous datasets. Processing an enormous amount of data is beyond the capacity of a single processor. The need of the hour is to develop fuzzy clustering techniques that can work on a distributed framework for Big Data processing and can handle heterogeneous data. In this research, we evaluate the performance of the recently proposed algorithm for the Fuzzy clustering of mixed-mode data FCMD-MD (D’Urso and Massari in Inf Sci 505:513–534, 2019) with different real-world datasets. We develop a distributed FCMD-MD, a fuzzy clustering algorithm for mixed-mode data in Apache SPARK. The experimental results show that the algorithm is scalable, performs well in a distributed environment, and clusters enormous heterogeneous data with high accuracy. We also compared the performance of distributed FCMD-MD and the distributed k-medoid algorithm.

The pitfalls of (non-definitive) Environmental, Social, and Governance scoring methodology

Evaluating companies' sustainability performance embraces environmental, social, and governance (ESG) activities. Data providers assign companies ESG scores as a quantitative measure based on available information. Refinitiv (previously ASSET4) is a key data provider whose scores are used extensively by researchers and companies; however, their ESG scoring methodology allows the scores from the five most recent years to change post-publication without any announcements. Such ESG scores are called non-definitive. Then, ESG research findings and companies' sustainability performance using the ESG data from the same data provider might be inconsistent. Optimization and exploratory data mining approaches show that it is possible to change ESG scores to exhibit stronger risk dependence. We discuss how the initial disclosure of ESG information and updating the published ESG information alter the way ESG scores are computed in a given industry group, impacting ESG research findings significantly. Moreover, the initial disclosure of ESG information and an update in the published ESG information might allow some companies to appear more sustainable, even though nothing has changed. Finally, our work indicates the criticality that should be addressed to improve comparability within research studies and companies' sustainability performance relying on data from the same ESG providers.

Initial Clustering Based on the Swarm Intelligence Algorithm for Computing a Data Density Parameter.

To improve the accuracy and efficiency of cluster startup using data density parameters, the author proposes a large data cluster extraction algorithm based on a herd intelligence algorithm. Since clustering to initiate data density parameters is primarily data mining, the author explores data mining clustering based primarily on herd intelligence algorithms. First, the obscure c-key cluster algorithm in the clustering algorithm is analyzed, and then the hybrid jump algorithm in the sub-heuristic herd intelligence optimization technology is optimized in the case of a few parameters by combining the obscure c-means cluster algorithm. The simulation results show that the convergence speed of the fuzzy C-means clustering algorithm and hybrid leapfrog algorithm is slow; the convergence rate of the PSO-FCM algorithm has been improved. Since the fusion algorithm requires fewer adjustment parameters, the cluster centers can be obtained more accurately and quickly with strong robustness and fast convergence. Compared with other algorithms, the fusion algorithm proposed by the author has the best performance in clustering effect, accuracy, convergence rate, and robustness. It is proved that the swarm intelligence algorithm can effectively perform density parameter initialization clustering on computational data.

Predicting breastfeeding practice of Nigerian child using machine learning and deep learning algorithms

The accessibility and rapid growth of data with the conspicuous rise in hardware technologies have led to the development of new studies in distributed and deep learning. This work reports the prediction of breastfeeding practice of a child using machine and deep learning algorithms. Despite that exclusive breastfeeding reduces the child mortality rate caused by pneumonia and diarrhea and other benefits attached to it the percentage of mothers who practice it in Nigeria still fall short to 29% in 2018 according to Nigeria Demographic Health Survey (NDHS) compare to the global target of 70% by 2030. The aim of the study is to develop a model for the prediction of breastfeeding practice in Nigeria. This study adopted Sample, Explore, Modify, Model and Access (SEMMA) data mining methodology using 2018 NDHS dataset. Experiments were performed with ML algorithms (RF, J48, JRIP, and SVM) built with WEKA software as well as with DNN algorithm using python. Some control parameters were applied to configure the DNN model while considering the number of layers, neurons within each layer, activation function for each layer, ADAM algorithm, epochs (iterations), learning rate and percentage split of dataset between training and testing subsets. The performance of the DNN model in predicting breastfeeding practice was evaluated and compared with previous study that used ML models only. It was found that DL has a better child prediction of breastfeeding practice than ML models with accuracy of 97.9%. This could serve as a supporting tool for healthcare practitioners to support breastfeeding practice in Nigeria.

Unraveling Spatial, Structural, and Social Country-Level Conditions for the Emergence of the Foreign Fighter Phenomenon: An Exploratory Data Mining Approach to the Case Of ISIS

ABSTRACT Governments face a tough and timeless challenge: dealing with the capability of radical terrorist organizations to recruit foreign fighters. However, scholars so far have ignored that this phenomenon pertains to the realm of complexity theory, failing to determine the combination of country-level variables able to catalyze this issue. This is an important concern if countries want to design effective socio-political strategies aimed at decreasing terrorist groups’ capability to enroll foreign fighters or, at least, to curtail the penetration of their radical message. Thus, to address this issue we undertake an exploratory data mining approach (knowledge discovery in databases) to discover country-level patterns which might engender conditions that induce people to join an extremist organization, based on the case of ISIS. After a pre-selection procedure, the 950 variables initially selected were reduced to 22, and subsequently used in decision tree algorithms. Findings reveal the existence of six specific country clusters, which are characterized by some spatial, structural (economic and political), and social variables that create favorable conditions for the emergence of the phenomenon. Academic and practical recommendations are then discussed.

Factors Associated With Unplanned Acute Care Services for Patients With Newly Diagnosed Hematologic Malignancies.

This study evaluated risk factors predicting unplanned 30-day acute service utilization among adults subsequent to hospitalization for a new diagnosis of leukemia, lymphoma, or myeloma. This study explored the prevalence of medical complications (aligned with OP-35 measure specifications from the Centers for Medicare & Medicaid Services [CMS] Hospital Outpatient Quality Reporting Program) and the potential impact of psychosocial factors on unplanned acute care utilization. This study included 933 unique patients admitted to three acute care inpatient facilities within a nonprofit community-based health care system in southern California from 2012 to 2017. Integrated comprehensive data elements from electronic medical records and facility oncology registries were leveraged for univariate statistics, predictive models constructed using multivariable logistic regression, and further exploratory data mining, with predictive accuracy of the models measured with c-statistics. The mean age of study participants was 65 years, and 55.1% were male. Specific diagnoses were lymphoma (48.7%), leukemia (35.2%), myeloma (14.0%), and mixed types (2.1%). Approximately one fifth of patients received unplanned acute care services within 30 days postdischarge, and over half of these patients presented with one or more symptoms associated with the CMS medical complication measure. The predictive models, with c-statistics ranging from 0.7 and above for each type of hematologic malignancy, indicated good predictive qualities with the impact of psychosocial functioning on the use of acute care services (P values < .05), including lack of consult for social work during initial admission (lymphoma or myeloma), history of counseling or use of psychotropic medications (lymphoma), and past substance use (myeloma). This study provides insights into patient-related factors that may inform a proactive approach to improve health outcomes, such as enhanced care transition, monitoring, and support interventions.

Exploratory Data Mining Techniques (Decision Tree Models) for Examining the Impact of Internet-Based Cognitive Behavioral Therapy for Tinnitus: Machine Learning Approach.

BackgroundThere is huge variability in the way that individuals with tinnitus respond to interventions. These experiential variations, together with a range of associated etiologies, contribute to tinnitus being a highly heterogeneous condition. Despite this heterogeneity, a “one size fits all” approach is taken when making management recommendations. Although there are various management approaches, not all are equally effective. Psychological approaches such as cognitive behavioral therapy have the most evidence base. Managing tinnitus is challenging due to the significant variations in tinnitus experiences and treatment successes. Tailored interventions based on individual tinnitus profiles may improve outcomes. Predictive models of treatment success are, however, lacking.ObjectiveThis study aimed to use exploratory data mining techniques (ie, decision tree models) to identify the variables associated with the treatment success of internet-based cognitive behavioral therapy (ICBT) for tinnitus.MethodsIndividuals (N=228) who underwent ICBT in 3 separate clinical trials were included in this analysis. The primary outcome variable was a reduction of 13 points in tinnitus severity, which was measured by using the Tinnitus Functional Index following the intervention. The predictor variables included demographic characteristics, tinnitus and hearing-related variables, and clinical factors (ie, anxiety, depression, insomnia, hyperacusis, hearing disability, cognitive function, and life satisfaction). Analyses were undertaken by using various exploratory machine learning algorithms to identify the most influencing variables. In total, 6 decision tree models were implemented, namely the classification and regression tree (CART), C5.0, GB, XGBoost, AdaBoost algorithm and random forest models. The Shapley additive explanations framework was applied to the two optimal decision tree models to determine relative predictor importance.ResultsAmong the six decision tree models, the CART (accuracy: mean 70.7%, SD 2.4%; sensitivity: mean 74%, SD 5.5%; specificity: mean 64%, SD 3.7%; area under the receiver operating characteristic curve [AUC]: mean 0.69, SD 0.001) and gradient boosting (accuracy: mean 71.8%, SD 1.5%; sensitivity: mean 78.3%, SD 2.8%; specificity: 58.7%, SD 4.2%; AUC: mean 0.68, SD 0.02) models were found to be the best predictive models. Although the other models had acceptable accuracy (range 56.3%-66.7%) and sensitivity (range 68.6%-77.9%), they all had relatively weak specificity (range 31.1%-50%) and AUCs (range 0.52-0.62). A higher education level was the most influencing factor for ICBT outcomes. The CART decision tree model identified 3 participant groups who had at least an 85% success probability following the undertaking of ICBT.ConclusionsDecision tree models, especially the CART and gradient boosting models, appeared to be promising in predicting ICBT outcomes. Their predictive power may be improved by using larger sample sizes and including a wider range of predictive factors in future studies.

Exploring data mining: facets and emerging trends

PurposeData mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself as an emerging field and manifests itself in the form of different techniques such as information mining; big data mining; big data mining and Internet of Things (IoT); and educational data mining. This paper aims to discuss how these technologies and techniques are used to derive information and, eventually, knowledge from data.Design/methodology/approachAn extensive review of literature on data mining and its allied techniques was carried to ascertain the emerging procedures and techniques in the domain of data mining. Clarivate Analytic’s Web of Science and Sciverse Scopus were explored to discover the extent of literature published on Data Mining and its varied facets. Literature was searched against various keywords such as data mining; information mining; big data; big data and IoT; and educational data mining. Further, the works citing the literature on data mining were also explored to visualize a broad gamut of emerging techniques about this growing field.FindingsThe study validates that knowledge discovery in databases has rendered data mining as an emerging field; the data present in these databases paves the way for data mining techniques and analytics. This paper provides a unique view about the usage of data, and logical patterns derived from it, how new procedures, algorithms and mining techniques are being continuously upgraded for their multipurpose use for the betterment of human life and experiences.Practical implicationsThe paper highlights different aspects of data mining, its different technological approaches, and how these emerging data technologies are used to derive logical insights from data and make data more meaningful.Originality/valueThe paper tries to highlight the current trends and facets of data mining.

BROCCOLI: overlapping and outlier-robust biclustering through proximal stochastic gradient descent

Matrix tri-factorization subject to binary constraints is a versatile and powerful framework for the simultaneous clustering of observations and features, also known as biclustering. Applications for biclustering encompass the clustering of high-dimensional data and explorative data mining, where the selection of the most important features is relevant. Unfortunately, due to the lack of suitable methods for the optimization subject to binary constraints, the powerful framework of biclustering is typically constrained to clusterings which partition the set of observations or features. As a result, overlap between clusters cannot be modelled and every item, even outliers in the data, have to be assigned to exactly one cluster. In this paper we propose Broccoli, an optimization scheme for matrix factorization subject to binary constraints, which is based on the theoretically well-founded optimization scheme of proximal stochastic gradient descent. Thereby, we do not impose any restrictions on the obtained clusters. Our experimental evaluation, performed on both synthetic and real-world data, and against 6 competitor algorithms, show reliable and competitive performance, even in presence of a high amount of noise in the data. Moreover, a qualitative analysis of the identified clusters shows that Broccoli may provide meaningful and interpretable clustering structures.