Adaptive Speech Information Hiding Method Based on K-Means

Persistent K-Means: Stable Data Clustering Algorithm Based on K-Means Algorithm

Identifying clusters or clustering is an important aspect of data analysis. It is the task of grouping a set of objects in such a way those objects in the same group/cluster are more similar in some sense or another. It is a main task of exploratory data mining, and a common technique for statistical data analysis This paper proposed an improved version of K-Means algorithm, namely Persistent K...

Hiding Information Under Speech

On Lloyd’s k-means Method∗

We present polynomial upper and lower bounds on the number of iterations performed by Lloyd’s method for k-means clustering. Our upper bounds are polynomial in the number of points, number of clusters, and the spread of the point set. We also present a lower bound, showing that in the worst case the k-means heuristic needs to perform Ω(n) iterations, for n points on the real line and two center...

Coverless Multi-keywords Information Hiding Method Based on Text

As a new information hiding method, coverless information hiding has become a hot issue in the field of information security. The existing coverless information hiding method can hide only one Chinese character in each natural text. However, the problem of the method is that the hiding capacity is too small. To address this problem, a new method named coverless multi-keywords information hiding...

Kernel Penalized K-means: A feature selection method based on Kernel K-means

Article history: Received 11 June 2014 Received in revised form 23 October 2014 Accepted 11 June 2015 Available online 19 June 2015