M: Cluster Analysis

2001

Citations in the corpus

• Computing Optimal Decision Sets with SAT (2020, background): “The complete space of feature values (or feature space [25]) is U , ∏Kr=1{fr,¬fr}.”
• Transfer defect learning (2013, methodology): “Normalization gives all features of a data set an equal weight and is thus known to be useful for classification algorithms [19].”
• Big data preprocessing: methods and prospects (2016, methodology): “The set of techniques used prior to the application of a data mining method is named as data preprocessing for data mining [16] and it is known to be one of the most meaningful issues within the famous Knowledge Discovery from Data process [17, 18] as shown in Fig.”
• Data Mining: A prediction for performance improvement using classification (2012, background): “Han and Kamber [6] describes data mining software that allow the users to analyze data from different dimensions, categorize it and summarize the relationships which are identified during the mining process.”
• DBSCAN Revisited, Revisited (2017, background): “It is discussed in textbooks [20, 21, 43] and was successfully used in many real-world applications.”
• Geovisual analytics for spatial decision support: Setting the research agenda (2007, background): “To cope with the problems of data size and dimensionality, synergetic links need to be established between the geovisualization and data mining (Hand et al. 2001, Han and Kamber 2006) and database technologies (Ramakrishnan and Gehrke 2003).”
• Cognitive Internet of Things: A New Paradigm Beyond Connection (2014, background): “In this article, several knowledge discovery techniques which are well established in the above listed disciplines are introduced as follows [46].”
• A SAT-Based Approach to Learn Explainable Decision Sets (2018, methodology): “Hence, the space of features (or feature space [14]) is U , ∏K r=1{fr,¬fr}.”
• Developing a Hybrid Intrusion Detection System Using Data Mining for Power Systems (2015, methodology): “For the work presented in this paper, the FP-growth [19]”
• An Overview of Artificial Intelligence Applications for Power Electronics (2020, background): “More unsupervised learning methods and theoretical details can be found in [136].”
• Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing (2018, methodology): “, in order to analyze the significance of the features, the Information Gain (IG) technique was applied [72].”
• Distance based similarity models for content based multimedia retrieval (2013, background): “Moreover, they are irreplaceable in many other research fields, such as data mining [Han et al., 2006], information retrieval [Manning et al., 2008], pattern recognition [Duda et al., 2001], computer vision [Szeliski, 2010], and also throughout all areas of database research.”
• A Review of Financial Accounting Fraud Detection based on Data Mining Techniques (2012, unknown): “s a model (on the target set) to predict the categorical labels of unknown objects to distinguish between objects of different classes. These categorical labels are predefined, discrete and unordered [24]. The research literature describes that classification or prediction is the process of identify”