classificationdata miningThe concept of Data Stream has emerged as a result of the evolution of technologies in different domains such as banking, eヽommerce, social media, and many others. It is defined as a sequence of data instances generated at very high speed, which can be hard to ...
The author considers a drift when the 30 number of classification errors occur. Here, the value of alpha and beta are 0.95 and 0.90, respectively. EDDM does not increase the false positive rate and detects the drift faster compared to the DDM. False positive rate is stored in “look-up ...
—This paper focuses on analyzing medical diagnostic data using classification rules in data mining and context reduction in formal concept analysis. It helps in finding redundancies among the various medical examination tests used in diagnosis of a disease. Classification rules have been derived from ...
22st IEEE International Conference on Data Mining (ICDM 2022) Title: Incremental classification and clustering, concept drift, novelty detection, active learning in big/fast data context Description: The development of dynamic information analysis methods, like incremental classification/clustering, concept ...
Some of the concepts and definitions, already familiar in concept recurrence studies of stream classification have been redefined for clustering. The experiments conducted on real and synthetic data streams reveal that the proposed algorithm has the potential to identify recurring concepts....
FCA has also been applied to text mining for discovering important themes and topics from large document collections. The identification of relevant concepts provides insights about the underlying structure of the data, which in turn helps develop more accurate document clustering and classification ...
Empirical evidence offers inspiring insights and demonstrates the proposed methodology is an advisable solution to prediction in data streams. 展开 关键词: classification concept change conceptual equivalence data stream proactive learning reactive learning ...
omas with predictive value. Since no single form of classification is appropriate for all data sets, a large toolkit of classification algorithms have been developed through the years (linear regression, logistic regression and naïve Bayes, among others)21,22. The underlying concept of this study...
The classification boundary depicted at time (t+1) represents the previously learned relationship between features and targets at time (t). Colors represent ground truth classes of the data points at the specified time step.This is seen in both Figure 3.a & 3.b above, where the distribution...
Data mining (DM) is the extraction of regularities from raw data, which are further transformed within the wider process of knowledge discovery in databases (KDD) into non-trivial facts intended to support decision making. Formal concept analysis (FCA) o