scholarly journals Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data

2009 ◽  
Vol 2009 ◽  
pp. 1-16 ◽  
Author(s):  
David J. Miller ◽  
Carl A. Nelson ◽  
Molly Boeka Cannon ◽  
Kenneth P. Cannon
Keyword(s):  
Fuzzy Clustering ◽  
Real World ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Optimum Number ◽  
Clustering Methods ◽  
Real World Data ◽  
Data Set ◽  
World Data

Fuzzy clustering algorithms are helpful when there exists a dataset with subgroupings of points having indistinct boundaries and overlap between the clusters. Traditional methods have been extensively studied and used on real-world data, but require users to have some knowledge of the outcome a priori in order to determine how many clusters to look for. Additionally, iterative algorithms choose the optimal number of clusters based on one of several performance measures. In this study, the authors compare the performance of three algorithms (fuzzy c-means, Gustafson-Kessel, and an iterative version of Gustafson-Kessel) when clustering a traditional data set as well as real-world geophysics data that were collected from an archaeological site in Wyoming. Areas of interest in the were identified using a crisp cutoff value as well as a fuzzyα-cut to determine which provided better elimination of noise and non-relevant points. Results indicate that theα-cut method eliminates more noise than the crisp cutoff values and that the iterative version of the fuzzy clustering algorithm is able to select an optimum number of subclusters within a point set (in both the traditional and real-world data), leading to proper indication of regions of interest for further expert analysis

Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology

2018 ◽  
Vol 8 (1) ◽  
pp. 42-59
Author(s):  
Deepali Virmani ◽  
Nikita Jain ◽  
Ketan Parikh ◽  
Shefali Upadhyaya ◽  
Abhishek Srivastav
Keyword(s):  
Unsupervised Learning ◽  
Real World ◽  
Learning Algorithms ◽  
Clustering Algorithms ◽  
Real World Data ◽  
World Data ◽  
Clustering Problem ◽  
Time Required ◽  
Selection Of

This article describes how data is relevant and if it can be organized, linked with other data and grouped into a cluster. Clustering is the process of organizing a given set of objects into a set of disjoint groups called clusters. There are a number of clustering algorithms like k-means, k-medoids, normalized k-means, etc. So, the focus remains on efficiency and accuracy of algorithms. The focus is also on the time it takes for clustering and reducing overlapping between clusters. K-means is one of the simplest unsupervised learning algorithms that solves the well-known clustering problem. The k-means algorithm partitions data into K clusters and the centroids are randomly chosen resulting numeric values prohibits it from being used to cluster real world data containing categorical values. Poor selection of initial centroids can result in poor clustering. This article deals with a proposed algorithm which is a variant of k-means with some modifications resulting in better clustering, reduced overlapping and lesser time required for clustering by selecting initial centres in k-means and normalizing the data.

scholarly journals Consensus Development of a Modern Ontology of Emergency Department Presenting Problems—The Hierarchical Presenting Problem Ontology (HaPPy)

2019 ◽  
Vol 10 (03) ◽  
pp. 409-420 ◽  
Author(s):  
Steven Horng ◽  
Nathaniel R. Greenbaum ◽  
Larry A. Nathanson ◽  
James C. McClay ◽  
Foster R. Goss ◽  
...  
Keyword(s):  
Emergency Department ◽  
Real World ◽  
Emergency Department Patient ◽  
Snomed Ct ◽  
Presenting Problems ◽  
Real World Data ◽  
Validation Data ◽  
Data Set ◽  
World Data ◽  
Academic Level

Objective Numerous attempts have been made to create a standardized “presenting problem” or “chief complaint” list to characterize the nature of an emergency department visit. Previous attempts have failed to gain widespread adoption as they were not freely shareable or did not contain the right level of specificity, structure, and clinical relevance to gain acceptance by the larger emergency medicine community. Using real-world data, we constructed a presenting problem list that addresses these challenges. Materials and Methods We prospectively captured the presenting problems for 180,424 consecutive emergency department patient visits at an urban, academic, Level I trauma center in the Boston metro area. No patients were excluded. We used a consensus process to iteratively derive our system using real-world data. We used the first 70% of consecutive visits to derive our ontology, followed by a 6-month washout period, and the remaining 30% for validation. All concepts were mapped to Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT). Results Our system consists of a polyhierarchical ontology containing 692 unique concepts, 2,118 synonyms, and 30,613 nonvisible descriptions to correct misspellings and nonstandard terminology. Our ontology successfully captured structured data for 95.9% of visits in our validation data set. Discussion and Conclusion We present the HierArchical Presenting Problem ontologY (HaPPy). This ontology was empirically derived and then iteratively validated by an expert consensus panel. HaPPy contains 692 presenting problem concepts, each concept being mapped to SNOMED CT. This freely sharable ontology can help to facilitate presenting problem-based quality metrics, research, and patient care.

scholarly journals Structure Identification-Based Clustering According to Density Consistency

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Chunzhong Li ◽  
Zongben Xu
Keyword(s):  
High Dimension ◽  
Real World ◽  
Clustering Algorithm ◽  
Density Difference ◽  
Structure Identification ◽  
Data Sets ◽  
Critical Importance ◽  
Real World Data ◽  
Data Set ◽  
High Dimension Data

Structure of data set is of critical importance in identifying clusters, especially the density difference feature. In this paper, we present a clustering algorithm based on density consistency, which is a filtering process to identify same structure feature and classify them into same cluster. This method is not restricted by the shapes and high dimension data set, and meanwhile it is robust to noises and outliers. Extensive experiments on synthetic and real world data sets validate the proposed the new clustering algorithm.

scholarly journals Data Analysis Using Representation Theory and Clustering Algorithms

2021 ◽  
Vol 19 ◽  
pp. 310-320
Author(s):  
Suboh Alkhushayni ◽  
Taeyoung Choi ◽  
Du’a Alzaleq
Keyword(s):  
Data Analysis ◽  
Random Forest ◽  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimal Number ◽  
Categorical Variables ◽  
Common Disease ◽  
Agglomerative Hierarchical Clustering ◽  
Data Set

This work aims to expand the knowledge of the area of data analysis through both persistence homology, as well as representations of directed graphs. To be specific, we looked for how we can analyze homology cluster groups using agglomerative Hierarchical Clustering algorithms and methods. Additionally, the Wine data, which is offered in R studio, was analyzed using various cluster algorithms such as Hierarchical Clustering, K-Means Clustering, and PAM Clustering. The goal of the analysis was to find out which cluster's method is proper for a given numerical data set. By testing the data, we tried to find the agglomerative hierarchical clustering method that will be the optimal clustering algorithm among these three; K-Means, PAM, and Random Forest methods. By comparing each model's accuracy value with cultivar coefficients, we came with a conclusion that K-Means methods are the most helpful when working with numerical variables. On the other hand, PAM clustering and Gower with random forest are the most beneficial approaches when working with categorical variables. All these tests can determine the optimal number of clustering groups, given the data set, and by doing the proper analysis. Using those the project, we can apply our method to several industrial areas such that clinical, business, and others. For example, people can make different groups based on each patient who has a common disease, required therapy, and other things in the clinical society. Additionally, for the business area, people can expect to get several clustered groups based on the marginal profit, marginal cost, or other economic indicators.

A retrospective analysis of treatment patterns in metastatic castration-resistant prostate cancer patients treated with radium-223.

2019 ◽  
Vol 37 (7_suppl) ◽  
pp. 180-180 ◽  
Author(s):  
A. Oliver Sartor ◽  
Sreevalsa Appukkuttan ◽  
Ronald E. Aubert ◽  
Jeffrey Weiss ◽  
Joy Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Real World ◽  
Pilot Trial ◽  
Clinical Trial Data ◽  
Castration Resistant Prostate Cancer ◽  
Real World Data ◽  
Data Set ◽  
World Data ◽  
Castration Resistant ◽  
Radium 223

180 Background: Radium-223 (RA-223) is the first FDA approved targeted alpha therapy that significantly improves overall survival (OS) in patients (pts) with metastatic castration resistant prostate cancer (mCRPC) with symptomatic bone metastases. There is limited real world data describing RA-223 current use. Methods: A retrospective patient chart review was done of men who received at least 1 cycle of Ra-223 for mCRPC in 10 centers throughout the US (4 academic, 6 private practices). All pts had a minimum follow-up of 4 months, or placed in hospice or death. Descriptive analyses for clinical characteristics and treatment outcomes were performed. Results: Among the 200 pts (mean age-73.6 years, mean Charlson comorbidity index-6.9) RA-223 was initiated on average 1.6 years from mCRPC diagnosis (first line use (1L)=38.5%, 2L=31.5% and ≥3L=30%). 78% completed 5-6 cycles of RA-223 with mean therapy duration of 4.2 months. Among all pts, 43% received RA-223 as monotherapy (no overlap with other mCRPC therapies) while 57% had combination therapy with either abiraterone or enzalutamide. Median OS following RA-223 initiation was 21.2 months (95% CI 19.6- 29.2). Table provides the RA-223 utilization by type of clinical practice. Conclusions: Utilization of RA-223 in this real world data set was distinct from clinical trial data. Most patients received RA-223 in combination with abiraterone or enzalutamide, therapies that were unavailable when the pilot trial was conducted. Median survival was 21.2 months. Real world use of RA-223 has evolved as newer agents have become FDA approved in bone-metastatic CRPC. Academic and community patterns of practice were more similar than distinct. [Table: see text]

Verification of statistical oncological endpoints on encrypted data: Confirming the feasibility of real-world data sharing without the need to reveal protected patient information.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18725-e18725
Author(s):  
Ravit Geva ◽  
Barliz Waissengrin ◽  
Dan Mirelman ◽  
Felix Bokstein ◽  
Deborah T. Blumenthal ◽  
...  
Keyword(s):  
Data Sharing ◽  
Real World ◽  
Clinical Decision Making ◽  
Homomorphic Encryption ◽  
Data Sets ◽  
Real World Data ◽  
Data Set ◽  
Raw Data ◽  
Encrypted Data ◽  
World Data

e18725 Background: Healthcare data sharing is important for the creation of diverse and large data sets, supporting clinical decision making, and accelerating efficient research to improve patient outcomes. This is especially vital in the case of real world data analysis. However, stakeholders are reluctant to share their data without ensuring patients’ privacy, proper protection of their data sets and the ways they are being used. Homomorphic encryption is a cryptographic capability that can address these issues by enabling computation on encrypted data without ever decrypting it, so the analytics results are obtained without revealing the raw data. The aim of this study is to prove the accuracy of analytics results and the practical efficiency of the technology. Methods: A real-world data set of colorectal cancer patients’ survival data, following two different treatment interventions, including 623 patients and 24 variables, amounting to 14,952 items of data, was encrypted using leveled homomorphic encryption implemented in the PALISADE software library. Statistical analysis of key oncological endpoints was blindly performed on both the raw data and the homomorphically-encrypted data using descriptive statistics and survival analysis with Kaplan-Meier curves. Results were then compared with an accuracy goal of two decimals. Results: The difference between the raw data and the homomorphically encrypted data results, regarding all variables analyzed was within the pre-determined accuracy range goal, as well as the practical efficiency of the encrypted computation measured by run time, are presented in table. Conclusions: This study demonstrates that data encrypted with Homomorphic Encryption can be statistical analyzed with a precision of at least two decimal places, allowing safe clinical conclusions drawing while preserving patients’ privacy and protecting data owners’ data assets. Homomorphic encryption allows performing efficient computation on encrypted data non-interactively and without requiring decryption during computation time. Utilizing the technology will empower large-scale cross-institution and cross- stakeholder collaboration, allowing safe international collaborations. Clinical trial information: 0048-19-TLV. [Table: see text]

A MULTI-CLUSTERING FUSION SCHEME FOR DATA PARTITIONING

2005 ◽  
Vol 15 (05) ◽  
pp. 391-401 ◽  
Author(s):  
DIMITRIOS S. FROSSYNIOTIS ◽  
CHRISTOS PATERITSAS ◽  
ANDREAS STAFYLOPATIS
Keyword(s):  
Clustering Algorithm ◽  
Optimal Number ◽  
Data Partitioning ◽  
Clustering Methods ◽  
Data Set ◽  
Fusion Procedure ◽  
Distinct Partition ◽  
Fusion Scheme ◽  
Previous Phase ◽  
Optimal Number Of Clusters

A multi-clustering fusion method is presented based on combining several runs of a clustering algorithm resulting in a common partition. More specifically, the results of several independent runs of the same clustering algorithm are appropriately combined to obtain a distinct partition of the data which is not affected by initialization and overcomes the instabilities of clustering methods. Subsequently, a fusion procedure is applied to the clusters generated during the previous phase to determine the optimal number of clusters in the data set according to some predefined criteria.

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