Co-clustering of Fuzzy Lagged Data

02/06/2014
by   Eran Shaham, et al.
0

The paper focuses on mining patterns that are characterized by a fuzzy lagged relationship between the data objects forming them. Such a regulatory mechanism is quite common in real life settings. It appears in a variety of fields: finance, gene expression, neuroscience, crowds and collective movements are but a limited list of examples. Mining such patterns not only helps in understanding the relationship between objects in the domain, but assists in forecasting their future behavior. For most interesting variants of this problem, finding an optimal fuzzy lagged co-cluster is an NP-complete problem. We thus present a polynomial-time Monte-Carlo approximation algorithm for mining fuzzy lagged co-clusters. We prove that for any data matrix, the algorithm mines a fuzzy lagged co-cluster with fixed probability, which encompasses the optimal fuzzy lagged co-cluster by a maximum 2 ratio columns overhead and completely no rows overhead. Moreover, the algorithm handles noise, anti-correlations, missing values and overlapping patterns. The algorithm was extensively evaluated using both artificial and real datasets. The results not only corroborate the ability of the algorithm to efficiently mine relevant and accurate fuzzy lagged co-clusters, but also illustrate the importance of including the fuzziness in the lagged-pattern model.

READ FULL TEXT

page 2

page 19

page 20

page 25

page 28

page 31

research
06/05/2018

A Visual Quality Index for Fuzzy C-Means

Cluster analysis is widely used in the areas of machine learning and dat...
research
08/01/2018

MaxMin Linear Initialization for Fuzzy C-Means

Clustering is an extensive research area in data science. The aim of clu...
research
07/11/2012

Similarity-Driven Cluster Merging Method for Unsupervised Fuzzy Clustering

In this paper, a similarity-driven cluster merging method is proposed fo...
research
04/11/2011

"Improved FCM algorithm for Clustering on Web Usage Mining"

In this paper we present clustering method is very sensitive to the init...
research
01/01/2021

Interval Type-2 Enhanced Possibilistic Fuzzy C-Means Clustering for Gene Expression Data Analysis

Both FCM and PCM clustering methods have been widely applied to pattern ...
research
04/13/2009

KiWi: A Scalable Subspace Clustering Algorithm for Gene Expression Analysis

Subspace clustering has gained increasing popularity in the analysis of ...
research
11/04/2022

Fuzzy Substring Matching: On-device Fuzzy Friend Search at Snapchat

About 50 friend to interact with. Since everyone has a unique list of fr...

Please sign up or login with your details

Forgot password? Click here to reset