Data Mining Concepts and Techniques

By S. Gnanapriya, R. Suganya, G. Devi, M. S. Kumar. 2010

3,233 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d76b49bb538e01576e744ab68033a9f643cc4483

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Luis Gravano
2. Paulo Rita
3. John Paparrizos
4. Sérgio Moro

In Information Systems

1. Xu Zhao
2. Chen-Fu Chien
3. Yun Fu
4. Luis Gravano
5. Paulo Rita
6. Hanhoon Kang
7. Bernardo Vala
8. Yuncai Liu

In Signal Processing

1. John Paparrizos
2. Luis Gravano
3. Yun Fu
4. Yixin Chen
5. Zhicheng Cui
6. Wayne Xin Zhao
7. Jiawei Jiang
8. Jingyuan Wang

Citations in the corpus

• Predicting social media performance metrics and evaluation of the impact on brand building: A data mining approach (2016, methodology): “A data mining approach typically includes phases such as data understanding, data preparation, modeling, and evaluation (Han et al., 2011).”
• Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews (2012, background): “Zhu et al. (2010) classified the Cornell movie review as positive or negative by using an ANNs-based individual model (i-model), which showed a better classification result than either the Support Vector Machine (SVM) or the Hidden Markov Model (HMM).”
• Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry (2008, background): “Data mining problems are generally categorized as association, clustering, classification, and prediction (Fayyad, Piatesky-Shapiro, & Smyth, 1996; Fu, 1997; Han & Kamber, 2001).”
• Comparisons Between Data Clustering Algorithms (2008, background): “It represents many data objects by few clusters, and hence, it models data by its clusters [3].”
• A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM) (2016, background): “Datamining is concernedwith discovering previously unknown and potentially useful patterns hidden in large volumes of data (Han and Kamber, 2001).”
• k-Shape: Efficient and Accurate Clustering of Time Series (2016, background): “Temporal, or sequential, data mining deals with problems where data are naturally organized in sequences [28].”
• Exploring discriminative pose sub-patterns for effective action classification (2013, methodology): “It is rule based and the association between frequent patterns and class label is used for classification [19].”