narative means

Generalized Steffensen Means

Clustering with Intelligent Linexk-Means

The intelligent LINEX k-means clustering is a generalization of the k-means clustering so that the number of clusters and their related centroid can be determined while the LINEX loss function is considered as the dissimilarity measure. Therefore, the selection of the centers in each cluster is not randomly. Choosing the LINEX dissimilarity measure helps the researcher to overestimate or undere...

Robots Solving Serial Means-Means-End Problems

Recently, Auersperg et al. [1] demonstrated that Goffin’s cockatoos (Cacatua goffini) are able to solve complex means-means-end problems. This is an impressive cognitive ability and it is desirable to build models to understand such abilities. In this paper we describe a project that models such behavior and recreates the experiment on a robotic platform. First preliminary results suggest that ...

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...

Knowledge Means 'All', Belief Means 'Most'

We introduce a bimodal epistemic logic intended to capture knowledge as truth in all epistemically alternative states and belief as a generalized ‘majority ’ quantifier, interpreted as truth in many (a ‘majority ’ of the) epistemically alternative states. This doxastic interpretation is of interest in KR applications and it also has an independent philosophical and technical interest. The logic...

RK-Means Clustering: K-Means with Reliability

This paper presents an RK-means clustering algorithm which is developed for reliable data grouping by introducing a new reliability evaluation to the K-means clustering algorithm. The conventional K-means clustering algorithm has two shortfalls: 1) the clustering result will become unreliable if the assumed number of the clusters is incorrect; 2) during the update of a cluster center, all the d...