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Data Mining and Knowledge Discovery
Kluwer Academic Publishers
1-10 of 46 reviews
Detecting cooperative and organized spammer groups in micro-blogging community
Dang Q., Zhou Y., Gao F., Sun Q. Data Mining and Knowledge Discovery 31(3): 573-605, 2017. Type: Article
Public relations (PR) companies hire and pay cooperative and organized spammer groups to post specific content on online microblogging sites, such as Twitter, to influence public opinion or trending topics (topic hijacking). Detecting such spammer...
Jan 4 2018
MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data
Becker M., Lemmerich F., Singer P., Strohmaier M., Hotho A. Data Mining and Knowledge Discovery 31(5): 1359-1390, 2017. Type: Article
This well-written paper includes adequate definitions to enable a layperson to understand the principles (generative processes of heterogeneous sequence data of human movement in a city) examined in its simulated experimental study. It uses approp...
Dec 7 2017
Tiers for peers: a practical algorithm for discovering hierarchy in weighted networks
Tatti N. Data Mining and Knowledge Discovery 31(3): 702-738, 2017. Type: Article
In the recent world of social networks and big data, the investigation of graph structures representing relationships between components of large datasets is a practically relevant and scientifically interesting question. The main aim of these app...
Nov 7 2017
Scalable density-based clustering with quality guarantees using random projections
Schneider J., Vlachos M. Data Mining and Knowledge Discovery 31(4): 972-1005, 2017. Type: Article
Efficient clustering techniques are required for knowledge discovery in large databases. The efforts of scientists have contributed to the development of many clustering algorithms....
Oct 30 2017
On searching and indexing sequences of temporal intervals
Kostakis O., Papapetrou P. Data Mining and Knowledge Discovery 31(3): 809-850, 2017. Type: Article
Have you ever wondered how it could be possible for a robot and its sensory system to understand obstacles and avoid them while randomly moving around? Did you ever ask yourself the questions of how signals can be captured and interpreted in a mea...
Oct 27 2017
Reducing uncertainty of dynamic heterogeneous information networks: a fusing reconstructing approach
Yang N., He L., Li Z., Yu P. Data Mining and Knowledge Discovery 31(3): 879-906, 2017. Type: Article
A detailed technical account, this paper presents a new method of handling heterogeneous information network analysis....
Aug 8 2017
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
Campos G., Zimek A., Sander J., Campello R., Micenková B., Schubert E., Assent I., Houle M. Data Mining and Knowledge Discovery 30(4): 891-927, 2016. Type: Article
This paper is a comprehensive study of the methods involved in determining the presence of outliers in datasets. There are few recent comparable works in the discipline, with most oriented toward data mining and various computer tools to solve for...
Jul 31 2017
Characterizing concept drift
Webb G., Hyde R., Cao H., Nguyen H., Petitjean F. Data Mining and Knowledge Discovery 30(4): 964-994, 2016. Type: Article
In data streams, such as stock market transactions, concept drift occurs when the relationship between the input data and target variable changes. For its different categories, the reader may refer to . This papers aims to provide a comprehensi...
May 10 2017
Using regression makes extraction of shared variation in multiple datasets easy
Korpela J., Henelius A., Ahonen L., Klami A., Puolamäki K. Data Mining and Knowledge Discovery 30(5): 1112-1133, 2016. Type: Article
This interesting paper presents an application that could be of value to individuals working in data analysis of sets, trying to find commonalities among what appears to be unrelated data. The idea behind the derivation of shared variation is mean...
Nov 23 2016
Multi-relational pattern mining over data streams
Silva A., Antunes C. Data Mining and Knowledge Discovery 29(6): 1783-1814, 2015. Type: Article
Mining large amounts of data in real time has been a great challenge. This paper deals with an important theme in this area and gives an algorithm for mining frequent relational patterns over data streams, being represented by batches of star-sche...
Apr 26 2016
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