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نتیجه جستجو - Data mining Applications

تعداد مقالات یافته شده: 11
ردیف عنوان نوع
1 Generalized fuzzy logic based performance prediction in data mining
پیش بینی عملکرد مبتنی بر منطق فازی تعمیم یافته در داده کاوی-2020
In recent days, the single and multiple economies depend upon the human capital to build a valuable service. The individual employee level is important to process and maintain the whole organization. Consequently, performance management is needed at each employee level and the business level to implement a system in order to measure the employee performance and provide growth based on the performance. In data mining applications, the knowledge discovery of interest in Human Resources Management (HRM) is applicable. To extract the knowledge significant data mining classification techniques were used. The scope of this work compares the predictive analyzing of theC4.5 algorithm, Naive Bayes and Fuzzy logics are made by comparing its accuracy. This paper proposed a framework to help human resource to monitor the employee performance. The exact accuracy of the proposed framework found to be more efficient in terms of the accurately predicting the outcome of the employee.© 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Advances in Materials Research – 2019.
Keywords: Employee performance prediction | Data mining | Naive Bayes | Fuzzy logics | Decision tree | C4.5algorithm
مقاله انگلیسی
2 A Cryptographic Ensemble for secure third party data analysis: Collaborative data clustering without data owner participation
یک گروه رمزنگاری برای تجزیه و تحلیل داده های شخص ثالث امن: خوشه بندی داده های مشارکتی بدون مشارکت صاحب داده-2019
This paper introduces the twin concepts Cryptographic Ensembles and Global Encrypted Distance Matrices (GEDMs), designed to provide a solution to outsourced secure collaborative data clustering. The cryptographic ensemble comprises: Homomorphic Encryption (HE) to preserve raw data privacy, while supporting data analytics; and Multi-User Order Preserving Encryption (MUOPE) to preserve the privacy of the GEDM. Clustering can therefore be conducted over encrypted datasets without requiring decryption or the involvement of data owners once encryption has taken place, all with no loss of accuracy. The GEDM concept is applicable to large scale collaborative data mining applications that feature horizontal data partitioning. In the paper DBSCAN clustering is adopted for illustrative and evaluation purposes. The results demonstrate that the proposed solution is both efficient and accurate while maintaining data privacy.
Keywords: Data mining as a service | Privacy preserving data mining | Security | Data outsourcing
مقاله انگلیسی
3 Optimized hardware accelerators for data mining applications on embedded platforms: Case study principal component analysis
شتاب دهنده سخت افزاری بهینه سازی شده برای برنامه های استخراج داده بر روی چهارچوب های embedded: مطالعه موردی تجزیه و تحلیل مؤلفه اصلی-2019
With the proliferation of mobile, handheld, and embedded devices, many applications such as data min- ing applications have found their way into these devices. However, mobile devices have stringent area and power limitations, high speed-performance, reduced cost, and time-to-market requirements. Furthermore, applications running on mobile devices are becoming more complex requiring high processing power. These design constraints pose serious challenges to the embedded system designers. In order to pro- cess the applications on mobile and embedded systems, effectively and efficiently, optimized hardware architectures are needed. We are investigating the utilization of FPGA-based customized hardware to ac- celerate embedded data mining applications including handwritten analysis and facial recognition. For these biometric applications, Principal Component Analysis (PCA) is applied initially, followed by similar- ity measure. In this research work, we introduce novel and efficient embedded hardware architectures to accelerate the PCA computation. PCA is a classic technique to reduce the dimensionality of data by transforming the original data set into a new set of variables called Principal Components (PCs) that rep- resent the key features of the data. We propose two hardware versions for PCA computation, each with its unique optimization techniques to enhance the performance of our designs, and one specifically with additional techniques to reduce the memory access latency of embedded platforms. To the best of our knowledge, we could not find similar work for PCA, specifically catered to the embedded devices, in the published literature. We perform experiments to evaluate the feasibility and efficiency of our designs us- ing a benchmark dataset for biometrics. Our embedded hardware designs are generic, parameterized, and scalable; and achieve 78 times speedup as compared to its software counterparts
Keywords: Data mining | Dimensionality reduction techniques | Embedded and mobile systems | FPGAs | Hardware acceleration | Principal Component Analysis
مقاله انگلیسی
4 A three-phase approach to differentially private crucial patterns mining over data streams
یک رویکرد سه فاز به الگوهای بسیار مهم خصوصی استخراج از جریان داده ها-2019
Frequent patterns mining over transactional data streams is an important task for a wide range of online data mining applications. Nevertheless, mining crucial patterns is even more appropriate than frequent patterns over transactional data streams, because crucial patterns are the subset of frequent patterns with the minimum storage cost and information lossless extraction. In this paper, we argue that the privacy of mining crucial patterns from data streams (i.e., aggregating information from individuals) is more likely to be leaked than static scenarios, due to successive releases. However, to the best of our knowledge, there is little work on differential privacy in continuously publishing crucial patterns from data streams. To this end, this paper proposes a real-time differentially private crucial pattern computation algorithm which designs a three-phase mechanism (i.e., the preprocessing phase, the deep-going calculation phase, and the noise-mining phase) at every timestamp. The algorithm is able to not only improve the utility of the crucial pattern statistics as much as possible which satisfy differential privacy, but also reduce the average mining time without incurring high maintenance cost according to the feature of crucial patterns. To reduce the number of calls to crucial pattern computation algorithm, we design two-dissimilarity formulas according to the relationship between frequent patterns and crucial patterns to decide to return either low noisy statistic or accurately approximated statistic in the first two phases. When the low noisy statistic needs to be turned, the algorithm goes into the noise-mining phase. To obtain private crucial patterns, we first filter crucial pattern candidate set by perturbing the scoring functions, and then add independent Laplace noise to their supports. Finally, we conduct extensive experiments on dense datasets and sparse datasets to show the effectiveness and efficiency of our algorithm.
Keywords: Differential privacy | Crucial patterns | Data streams | Privacy leakage | Data mining
مقاله انگلیسی
5 Classifying longevity profiles through longitudinal data mining
طبقه بندی پروفایل های طول عمر از طریق داده کاوی طولی-2019
Populational studies of human ageing often generate longitudinal datasets with high dimensionality. In order to discover knowledge in such datasets, the traditional knowledge discovery in database task needs to be adapted. In this article, we present a full knowledge discovery process that was performed on a lon- gitudinal dataset, mentioning the singularities of this process. We investigated the English Longitudinal Study of Ageing’s (ELSA’s) database, employing both semi-supervised and supervised learning techniques to determine and describe the profiles of individuals annotated with the class labels “short-lived”and “long-lived”who participated in the study. We report on the data preprocessing, the clustering task of finding the best sets of representatives of the profiles of each class, and the use of supervised learning to describe these profiles and perform a longitudinal classification on the dataset to investigate how consis- tently the unlabelled records would fit into the classes. The results show that several aspects are used to discriminate the individuals between the longevity profiles. Those aspects include economic, social and health-related attributes. The findings have pointed towards a need to further investigate the relation- ships between the different aspects, especially those related to physical health and wellbeing, and how they affect the lifespan of an individual. Furthermore, our methodology and the adopted procedures can be applied to any other data mining applications for longitudinal studies of ageing.
Keywords: Machine learning | Longitudinal data | Cluster analysis | Ageing studies
مقاله انگلیسی
6 An algorithm for learning shape and appearance models without annotations
الگوریتمی برای یادگیری مدل های شکل و ظاهر بدون حاشیه نویسی-2019
This paper presents a framework for automatically learning shape and appearance models for medical (and certain other) images. The algorithm was developed with the aim of eventually enabling distributed privacy-preserving analysis of brain image data, such that shared information (shape and appearance ba- sis functions) may be passed across sites, whereas latent variables that encode individual images remain secure within each site. These latent variables are proposed as features for privacy-preserving data mining applications. The approach is demonstrated qualitatively on the KDEF dataset of 2D face images, showing that it can align images that traditionally require shape and appearance models trained using manually annotated data (manually defined landmarks etc.). It is applied to the MNIST dataset of handwritten digits to show its potential for machine learning applications, particularly when training data is limited. The model is able to handle “missing data”, which allows it to be cross-validated according to how well it can predict left-out voxels. The suitability of the derived features for classifying individuals into patient groups was assessed by applying it to a dataset of over 1900 segmented T1-weighted MR images, which included images from the COBRE and ABIDE datasets
Keywords: Machine learning | Latent variables | Diffeomorphisms | Geodesic shooting | Shape model | Appearance model
مقاله انگلیسی
7 A pure array structure and parallel strategy for high-utility sequential pattern mining
یک ساختار آرایه خالص و استراتژی موازی برای کاوش الگوهای متوالی سود بالا-2018
High-utility sequential pattern mining (HUSPM) is the task of discovering all sequential patterns in a sequence database whose utility values are equal to or greater than a given minimum utility thresh old. HUSPM has become increasingly important in many real-world data mining applications, such as market basket data analysis, weblog mining, and bio-medical gene data analysis, which considers co occurrence values and quantity, utility (e.g., profit or cost) and time. Current approaches in the literature for HUSPM use the utility matrix to store a sequence database in the memory. Unfortunately, the utility matrix consumes a large amount of main memory. To address this issue, we introduce a pure array struc ture that reduces the memory consumption when compared to the utility matrix. In addition, HUSPM is also challenged with the downward closure property (DCP) to prune the search space. Recently, HUSPM algorithms have used the upper bound of utility values as the DCP. However, it is usually higher than the actual utility of patterns. Thus, these algorithms may generate many candidate patterns. The large search space leads to poor performance due to excessive runtime and memory usage. One of the reasons is the number of candidate patterns is proportional to the number of requisite projected database scans for calculating their actual utilities. In this paper, we present a novel pruning strategy that efficiently prunes non-HUSPs and significantly reduces the search space compared to the state-of-the-art HUS-Span algorithm. Moreover, we propose a parallel strategy to speed up the mining process. Then, we propose two algorithms which are the pure Array structure for High-utility Sequential (AHUS) pattern mining and AHUS parallel mining (AHUS-P). The AHUS-P algorithm uses shared memory to parallelize the mining process. It concurrently identifies HUSPs based on the advantages of the multi-core processor architec ture. The experimental results show that AHUS and AHUS-P can efficiently and effectively discover all HUSPs. Both the proposed algorithms outperform the state-of-the-art HUS-Span algorithm in terms of runtime, memory usage, and scalability for all experimental datasets.
Keywords: Data mining ، Sequential pattern mining ، High-utility sequential pattern mining ، (HUSPM) ، Parallel mining ، Shared-memory parallel
مقاله انگلیسی
8 یک مرور کلی از برنامه های کاربردی داده کاوی در بهداشت و درمان
سال انتشار: 2016 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 18
تکامل اطلاعات موجود در سیستم های بهداشتی وجود دارد. با این حال، ابزار روانکاوی مؤثر برای کشف روابط پنهان و روند اطلاعات وجود دارد. به وضوح توانایی های کامل داده های جمع آوری شده در سازمان بهداشتی و درمان به عنوان داده های تحت تجزیه و تحلیل فوق العاده بزرگ، با ابعاد زیاد، توزیع شده و نا مشخص بدون داده کاوی دشوار است. هدف این مطالعه، کشف مناطق جدید در زمینه تکنیک های داده کاوی مورد استفاده در مدیریت مراقبت های بهداشتی است. این مقاله با هدف ارائه گزارش دقیق از انواع مختلف برنامه های کاربردی داده کاوی در منطقه بهداشت و درمان و به حداقل رساندن پیچیدگی مطالعه درک اطلاعات سلامت پرداخته است.
کلمات کلیدی: داده کاوی
مقاله ترجمه شده
9 Literature Review of Data Mining Applications in Academic Libraries
بررسی ادبیات نرم افزار داده کاوی در کتابخانه های دانشگاهی-2015
This article provides a comprehensive literature review and classification method for data mining techniques applied to academic libraries. To achieve this, forty-one practical contributions over the period 1998–2014 were identified and reviewed for their direct relevance. Each article was categorized according to the main data mining functions: clustering, association, classification, and regression; and their application in the four main library aspects: services, quality, collection, and usage behavior. Findings indicate that both collection and usage behavior analyses have received most of the research attention, especially related to collection development and usability of websites and online services respectively. Furthermore, classification and regression models are the two most commonly used data mining functions applied in library settings.Additionally, results indicate that the top 6 journals of articles published on the application of data mining techniques in academic libraries are: College and Research Libraries, Journal of Academic Librarianship, Informa- tion Processing and Management, Library Hi Tech, International Journal of Knowledge, Culture and Change Management, and The Electronic Library. Scopus is the multidisciplinary database that provides the best coverage of journal articles identified. To our knowledge, this study represents the first systematic, identifiable and comprehensive academic literature review of data mining techniques applied to academic libraries.© 2015 Elsevier Inc. All rights reserved.
Keywords: Data mining | Bibliomining | Literature review | Academic libraries
مقاله انگلیسی
10 مروری بر تکنیک های داده کاوی برای مدیریت ارتباط با مشتری
سال انتشار: 2014 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 21
CRM یک نیاز اساسی برای هر سازمانی برای حفظ و جذب مشتریان ارزشمند است. در دنیای شرکت های بزرگ، استراتژی "حفظ مشتری" در CRM یک موضوع به طور فزاینده فشرده و مهم است. برای CRM بهتر، تکنیک های داده کاوی نقش حیاتی با استخراج اطلاعات مشتری از پایگاه داده ها بازی میکنند. داده کاوی می تواند به بخشهای خدمات مانند بانکداری، بیمه، و ارتباطات از راه دور کمک کند و برای تصمیم گیریهای کسب و کار نیز حیاتی است. هدف از این مقاله به طور خلاصه بررسی در برنامه های کاربردی داده کاوی در حوزه مدیریت ارتباط با مشتری می باشد. در این مقاله به بررسی چگونگی تکنیک های داده کاوی مانند ابزارk مانند ، SVM، درخت تصمیمگیری، شبکه عصبی و غیره پرداخته میشود که برای حمایت از فرایند مدیریت ارتباط با مشتری نعمیم داده شده است.
کلمات کلیدی: داده کاوی | کاربردهای داده کاوی | مدیریت ارتباط با مشتری | بررسی | CRM
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