Data Mining - Concepts and Techniques

By P. Perner. Künstliche Intell., 2002

17,180 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/982b955c900b04e9da64e3b39422690c13d6b94f

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Citations in the corpus

A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data (2013, background): “Clustering is one of the important and well-known data mining tasks (Han and Kamber 2001; Pang-ning et al. 2006).”
A Survey and Evaluation of the Potentials of Distributed Ledger Technology for Peer-to-Peer Transactive Energy Exchanges in Local Energy Markets (2019, unknown): “The process to find a solution to this puzzle is called mining [71].”
A Review of Financial Accounting Fraud Detection based on Data Mining Techniques (2012, unknown): “This attribute is referred as the predicted attribute [24].”
Mapping the Gnutella Network: Properties of Large-Scale Peer-to-Peer Systems and Implications for System Design (2002, background): “Note that a large class of data mining and machine learning algorithms based on information gains (ID3, C4.5, etc. [17]) use a similar argument to build their decision trees.”
Data Mining: A Preprocessing Engine (2006, background): “So, data integration must be done carefully to avoid redundancy and inconsistency that in turn improve the accuracy and speed up the mining process([2]).”
Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey (2016, methodology): “In data-mining and machine-learning terminology, such activities are called directed, and use supervised learning tools such as classification, decision trees, and artificial neural networks [28].”
Study on density peaks clustering based on k-nearest neighbors and principal component analysis (2016, methodology): “Clustering methods are generally divided into ﬁve groups: hierarchical clustering, partitioning clustering, density-based clustering, grid-based clustering and model-based clustering [2] .”
Fraud detection system: A survey (2016, methodology): “Furthermore, data mining has incorporated many techniques from other domains such as statistics, machine learning, pattern recognition, database and data warehouse systems, information retrieval, visualization, algorithms, high-performance computing, and many application domains (Han et al., 2012).”
Travel time estimation of a path using sparse trajectories (2014, methodology): “For example, we can mine frequent patterns from historical trajectories [6, 7, 12] in advance, and then use the average travel time of a pattern to represent the travel of the path corresponding to the pattern.”
Querying and mining of time series data: experimental comparison of representations and distance measures (2008, background): “Section 2 reviews the concept of time series, and gives an overview of the deﬁnitions of diﬀerent representation techniques and similarity measures investigated in this work.”
Text summarization using a trainable summarizer and latent semantic analysis (2005, methodology): “We measured the performance of our first approach in the way called k-fold cross-validation (Han & Kamber, 2001).”
Data Mining in Healthcare and Biomedicine: A Survey of the Literature (2012, background): “However, many researchers believe data mining to be one of the major steps of the KDD process [6, 9, 10 ].”
Human gesture recognition using Kinect camera (2012, background): “In the near future, we plan to augment the knowledge of this recognition in time series analysis of a stream of human gestures in fall motion detection for practical and affordable smart home applications.”
Computational Methods in Drug Discovery (2014, background): “The order in which features are divided is usually based on an information gain (difference between information before and after the branching) parameter with the highest valued features appearing first (Mitchell, 1997; Han and Kamber, 2006).”