Bitcoin concepts, threats, and machine-learning security solutions
The concept of Bitcoin was first introduced by an unknown individual (or a group of people)
named Satoshi Nakamoto before it was released as open-source software in 2009. Bitcoin is …
named Satoshi Nakamoto before it was released as open-source software in 2009. Bitcoin is …
Clustering approaches for high‐dimensional databases: A review
Data mining is an inevitable task in most of the emerging computing technologies as it
debilitates the complexity of datasets by rendering a better insight. Moreover, it entails the …
debilitates the complexity of datasets by rendering a better insight. Moreover, it entails the …
Learning vector-quantized item representation for transferable sequential recommenders
Recently, the generality of natural language text has been leveraged to develop transferable
recommender systems. The basic idea is to employ pre-trained language models (PLM) to …
recommender systems. The basic idea is to employ pre-trained language models (PLM) to …
Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model
Conventional supervised and unsupervised machine learning models used for landslide
susceptibility prediction (LSP) have many drawbacks, such as an insufficient number of …
susceptibility prediction (LSP) have many drawbacks, such as an insufficient number of …
K-means properties on six clustering benchmark datasets
P Fränti, S Sieranoja - Applied intelligence, 2018 - Springer
This paper has two contributions. First, we introduce a clustering basic benchmark. Second,
we study the performance of k-means using this benchmark. Specifically, we measure how …
we study the performance of k-means using this benchmark. Specifically, we measure how …
[PDF][PDF] Comparative analysis of k-means and fuzzy c-means algorithms
S Ghosh, SK Dubey - … Journal of Advanced Computer Science and …, 2013 - Citeseer
In the arena of software, data mining technology has been considered as useful means for
identifying patterns and trends of large volume of data. This approach is basically used to …
identifying patterns and trends of large volume of data. This approach is basically used to …
Understanding of internal clustering validation measures
Clustering validation has long been recognized as one of the vital issues essential to the
success of clustering applications. In general, clustering validation can be categorized into …
success of clustering applications. In general, clustering validation can be categorized into …
A study of probabilistic password models
A probabilistic password model assigns a probability value to each string. Such models are
useful for research into understanding what makes users choose more (or less) secure …
useful for research into understanding what makes users choose more (or less) secure …
Understanding and enhancement of internal clustering validation measures
Clustering validation has long been recognized as one of the vital issues essential to the
success of clustering applications. In general, clustering validation can be categorized into …
success of clustering applications. In general, clustering validation can be categorized into …
Fast density clustering strategies based on the k-means algorithm
Clustering by fast search and find of density peaks (CFSFDP) is a state-of-the-art density-
based clustering algorithm that can effectively find clusters with arbitrary shapes. However, it …
based clustering algorithm that can effectively find clusters with arbitrary shapes. However, it …