Contributed 4: Big Data
Wednesday June 7 // Room 1 // 15:00

46

Contributed 4: Big Data
Wednesday June 7 // Room 1 // 15:20

47

Whole-neighbourhood similarity measures permit big-data
analysis with controllable bias

K-means clustering using an improved firefly algorithm
applied to real world data sets

Tom Lorimer1, Ruedi Stoop1,2
1 Institute of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland

Masaki Takeuchi1, Thomas Ott2, Haruna Matsushita1, Yoko Uwate1,Yoshifumi Nishio1

2 Institute for Computational Science, University of Zurich, Switzerland

1 Tokushima University, Japan
2 Institute of Applied Simulation, ZHAW Zurich University of Applied Sciences,

A first step in the analysis of data typically is to look for structure. This
often begins with the search for ‘clusters', sets of data points made upby ‘similar’ data. Recent work has revealed, however, that many
popular clustering algorithms identify clusters that are inconsistent withreal-world data structures. This is because cluster tags do not containthe full relevant data structure, and because even advanced present
day data clustering approaches modify the underlying data structures ina largely unpredictable manner. We present a novel framework basedon graphs, for the identification and representation of arbitrary highdimensional data structure. In the approach, we apply wholeneighbourhood non-iterative measures of similarity between nodes ona k-nearest-neighbour graph over the data. In this way, we can prune
non-representative edges in a tuneable manner, taking into accountspecific interest that an observer has in the data. From the resulting
graphs, standard network characteristics can be extracted. In
conjunction with a novel visualisation scheme, this approach is fully
general, transparent, controllable and interpretable. We validate ourapproach on synthetic and natural data sets, exhibiting different
strengths of the proposed method.

Switzerland

K-means clustering is a type of unsupervised learning, which is wellused when the number of clusters is known and the clusters tend to bespherical. The goal of the K-means algorithm is to find K cluster centers
and assign each object to the closest cluster center such that the sumof the squared distances between the objects and the corresponding
cluster centers is minimal. This means that the K-means clustering
problem is a minimum optimization problem.
The firefly algorithm (FA) has been proposed by Yang and is idealized
the flashing characteristics of fireflies. FA is an efficient optimization
algorithm because it has a deterministic component and a random
component. In 2011, Senthilinath et al. proposed a new algorithm
combined the firefly algorithm and the K-means algorithm (KMFA).
Numerical experiments have indicated that this algorithm is more
efficient than the K-means algorithm or other improved algorithms.
In this study, we propose a new algorithm that used improved fireflyalgorithm for the K-means clustering (KMIFA). In this algorithm, eachfirefly has its own new parameter. The new parameter has a following
effects on the movement of firefly. At the beginning of the search, allfireflies move with a relatively strong random influence. Hence, they
can more easily escape from local optima. As number of iterations
increases, the firefly does not move randomly. Therefore, the fireflytends to converge. We compare the K-means algorithm, KMFA andKMIFA using several data sets. These experiments indicate that ouralgorithm is more efficient than the other algorithms.