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Collaborative Data Analytics for a Smart World
Published in Nishu Gupta, Srinivas Kiran Gottapu, Rakesh Nayak, Anil Kumar Gupta, Mohammad Derawi, Jayden Khakurel, Human-Machine Interaction and IoT Applications for a Smarter World, 2023
Rani Deepika Balavendran Joseph
Data mining techniques are used in various fields such as retail, finance, and communication. These techniques are classified as supervised and unsupervised techniques. Supervised learning technique learns from the historic data and develops a relation between input and output, whereas unsupervised learning technique directed by an explicit target. The primary motive of this unsupervised learning is to recognize unknown structures of data. This will help in the growth of individuals sectors to provide better shared environment. Virtual cloud model framework can be used for proper storing, computing, and visualizing of data. It is visualizing and interpreting the analyzed data by considering constraints like security and management of data [3]. There are a few common steps that need to be followed for analyzing and interpreting data.
An Optimal Diabetic Features-Based Intelligent System to Predict Diabetic Retinal Disease
Published in Ayodeji Olalekan Salau, Shruti Jain, Meenakshi Sood, Computational Intelligence and Data Sciences, 2022
M. Shanmuga Eswari, S. Balamurali
Data preprocessing is a data mining approach that entails converting raw data into a format that can be understood. Real-world data are frequently inadequate, inconsistent, and lacking in specific behaviors or trends to include numerous inaccuracies. Data preprocessing is a simple, but effective way of resolving such challenges. Generally, preprocessing involves the following steps: Data consolidation that entails gathering, selecting, and integrating information on diabetic patients’ data.Data cleaning that entails substituting values and removing duplicates from the new dataset.Normalization and discretization of data and construction of attributes in the dataset.Data reduction that consists of reducing dimension based on principle components.
Case Studies
Published in Abhijit Pandit, Mathematical Modeling using Fuzzy Logic, 2021
Data mining is the process of discovering patterns in large datasets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to periscope information (with intelligent methods) from a dataset and transform the information into a comprehensible structure for remoter use. Data mining is the wringer step of the “knowledge discovery in databases” process, or KDD. Aside from the raw wringer step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The difference between data wringer and data mining is that data wringer is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the value of data, whereas data mining uses machine learning and statistical models to uncover underhand or subconscious patterns in a large volume of data.
Applying transfer learning to achieve precision marketing in an omni-channel system – a case study of a sharing kitchen platform
Published in International Journal of Production Research, 2021
Ming-Chuan Chiu, Kai-Hsiang Chuang
One well-known technique involving AI is data mining, defined by Berson, Smith, and Thearling (2000) as the process of detecting and extracting hidden patterns of information from databases. Data mining is utilised to uncover high-dimensional correlations in datasets and to extract information, or to transform those findings into a structure for further use. Some emerging applications services, such as data warehousing and online services on the Internet, also involve various data mining techniques to help better understand user behaviours, to improve the offered services, and to increase business opportunities (Chen, Han, and Yu 1996). With the rapid development of the Internet, big data is quickly expanding into all areas of science and engineering (Wu et al. 2014). Wang, Kung, and Byrd (2018) singled out five big data analytics capabilities for generating business value: analytical capability for patterns of care, unstructured data analytical capability, decision support capability, predictive capability, and traceability. Using a comprehensive customer database, data mining can search for potential patterns of customer use and preference and can then indicate new opportunities for achieving successful future business intelligence. Spiess et al. (2014) adopted data mining to improve customer experience and business performance in the field of communications service providers. The goal of data mining in marketing is to achieve strategic business value, which provides a competitive advantage for the business (Grover et al. 2018).
Medical Internet of things using machine learning algorithms for lung cancer detection
Published in Journal of Management Analytics, 2020
Kanchan Pradhan, Priyanka Chawla
The key intention of IoT is to make the surroundings smarter, by providing the required data from historical or real basis and implement computational intelligence automatically for taking smart decisions. Multiple types of research were reported in the existing contributions and those are on the basis of various techniques have the capacity to enable the early detection and prognosis (Arunkumar & Ramakrishnan, 2019; Pati, 2019). Data mining generally consists of many approaches like association rule mining, NN, DT, etc. Every method evaluates the information in varied conducts (Yu, Ni, Dan, & Xu, 2012; Zhang, Qi, et al., 2019). The information related to lung cancer taken from IoT devices are utilized for knowing and managing difficult environments, allowing great automation, more efficiency, accuracy, wealth generation, productivity, and better decision making (Das et al., 2019). In these environments, a significant challenge is the timely processing of huge amounts of data for delivering highly steadfast and accurate observations and decisions so that IoT can fulfill its promise.
Association rule learning to improve deficiency inspection in port state control
Published in Maritime Policy & Management, 2020
Wu-Hsun Chung, Sheng-Long Kao, Chun-Min Chang, Chien-Chung Yuan
Data mining is a computational process of extracting useful information from large data sets involving various methods, such as statistics, artificial intelligence, database systems, etc. It has been widely applied in various fields, such as finance, manufacturing, healthcare, biology, and so on. Data mining includes many different types of functions, including generalization, association analysis, classification, cluster analysis, and outlier analysis. In this paper, association analysis is the main focus of our analysis of PSC records. Association analysis refers to a rule-based machine learning process for identifying specific relations between variables in data sets. A well-known case is the analysis of sales records from the retailer giant, Walmart. Using this association analysis, Walmart found that many young male customers who bought diapers also bought beer at certain times. A seemingly-impossible correlation between two items was identified. Such association analysis can be also applied to PSC on-board inspections to identify relations among certain deficiency items. Once one deficiency item is identified, the other correlated items can be more easily and are more likely to be identified to improve the effectiveness and efficiency of PSC inspection.