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Data Mining and Knowledge Discovery
Kluwer Academic Publishers
  1-10 of 46 reviews Date Reviewed 
  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 [1]. 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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