Advances in K-means Clustering
A Data Mining Thinking
Page
%P
13 Result(s)
-
Article
Fraud detection via behavioral sequence embedding
Fraud detection is usually compared to finding a needle in a haystack and remains a challenging task because fraudulent acts are buried in massive amounts of normal behavior and true intentions may be disguise...
-
Article
Intelligent bus routing with heterogeneous human mobility patterns
Optimal planning for public transportation is one of the keys hel** to bring a sustainable development and a better quality of life in urban areas. Compared to private transportation, public transportation u...
-
Chapter and Conference Paper
K-core-preferred Attack to the Internet: Is It More Malicious Than Degree Attack?
K-core (k-shell) index is an interesting measure that describes the core and fringe nodes in a complex network. Recent studies have revealed that some high k-core value nodes may play a vital role in informati...
-
Article
Information propagation in online social networks: a tie-strength perspective
In this paper, we investigate the relationship between the tie strength and information propagation in online social networks (OSNs). Specifically, we propose a novel information diffusion model to simulate th...
-
Book
-
Chapter
The Uniform Effect of K-means Clustering
This chapter studies the uniform effect of K-means clustering. As a well-known and widely used partitional clustering method, K-means has attracted great research interests for a very long time. Researchers have ...
-
Chapter
Information-Theoretic K-means for Text Clustering
Information-theoretic clustering aims to exploit information-theoretic measures as the clustering criteria. A common practice on this topic is the so-called Info-Kmeans, which performs K-means clustering with ...
-
Chapter
K-means Based Local Decomposition for Rare Class Analysis
Given its importance, the problem of predicting rare classes in large-scale multi-labeled data sets has attracted great attentions in the literature. However, rare class analysis remains a critical challenge, ...
-
Chapter
Cluster Analysis and K-means Clustering: An Introduction
The phrase “data mining” was termed in the late eighties of the last century, which describes the activity that attempts to extract interesting patterns from data. Since then, data mining and knowledge discovery ...
-
Chapter
Generalizing Distance Functions for Fuzzy c-Means Clustering
Fuzzy \(c\) -means (FCM) is a well-known partitional clustering method, which allows an object to belong to two or more clusters with a me...
-
Chapter
Selecting External Validation Measures for K-means Clustering
Cluster validity is a long standing challenge in the clustering literature. While many evaluation measures have been developed for cluster validity, these measures often provide inconsistent information abou...
-
Chapter
K-means Based Consensus Clustering
Consensus clustering, also known as cluster ensemble or clustering aggregation, aims to find a single clustering from multi-source basic clusterings on the same group of data objects. It has been widely recognize...
-
Article
COG: local decomposition for rare class analysis
Given its importance, the problem of predicting rare classes in large-scale multi-labeled data sets has attracted great attention in the literature. However, rare class analysis remains a critical challenge, b...
