EVOLVING A STIGMERGIC
SELF-ORGANIZED DATA-MINING
Vitorino Ramos
CVRM-GeoSystems Centre,
IST, Technical University of Lisbon, PORTUGAL
vitorino.ramos@alfa.ist.utl.pt

Ajith Abraham
Natural Computation Lab, Department of Computer Science,
Oklahoma State University, Tulsa, OK, USA
aa@cs.okstate.edu
ABSTRACT
Self-organizing complex systems typically are comprised of a large number of frequently similar components or events.
Through their process, a pattern at the global-level of a system emerges solely from numerous interactions among the
lower-level components of the system. Moreover, the rules specifying interactions among the system’s components are
executed using only local information, without reference to the global pattern, which, as in many real-world problems is
not easily accessible or possible to be found. Stigmergy, a kind of indirect communication and learning by the
environment found in social insects is a well know example of self-organization, providing not only vital clues in order to
understand how the components can interact to produce a complex pattern, as can pinpoint simple biological non-linear
rules and methods to achieve improved artificial intelligent adaptive categorization systems, critical for Data-Mining. On
the present work it is our intention to show that a new type of Data-Mining can be designed based on Stigmergic
paradigms, taking profit of several natural features of this phenomenon. By hybridizing bio-inspired Swarm Intelligence
with Evolutionary Computation we seek for an entire distributed, adaptive, collective and cooperative self-organized
Data-Mining. As a real-world / real-time test bed of our proposal, World-Wide-Web Mining will be used. Having that
purpose in mind, Web usage Data was collected from the Monash University’s Web site (Australia), with over 7 million
hits every week. Results are compared to other recent systems, showing that the system presented is by far promising.
KEYWORDS
Self-organization, Stigmergy, Data-Mining, Linear Genetic Programming, Distributed and Collaborative Filtering.
1. INTRODUCTION
Self-Organization refers to a broad range of pattern-formation processes in both physical and biological
systems, such as sand grains assembling into rippled dunes, chemical reactants forming swirling spirals, cells
making up highly structured tissues, and fish joining together in schools. A basic feature of these diverse
systems is the means by which they acquire their order and structure. In self-organizing systems, pattern
formation occurs through interactions internal to the system, without intervention by external directing
influences. As defined by Camazine et al [29], self-organization is a process in which pattern at the global
level of a system emerges solely from numerous interactions among the lower-level components of the
system. Moreover, the rules specifying interactions among the system’s components are executed using only
local information, without reference to the global pattern.
One well know example is provided by the emergence of self-organization in social insects, via direct
(mandibular, antennation, chemical or visual contact, etc) or indirect interactions. The latter types are more
subtle and defined by Grassé as stigmergy [11] to explain task coordination and regulation in the context of
nest reconstruction in Macrotermes termites. An example [10], could be provided by two individuals, who
interact indirectly when one of them modifies the environment and the other responds to the new
environment at a later time. In other words, stigmergy could be defined as a typical case of environmental

synergy or learning via the environment. Grassé showed that the coordination and regulation of building
activities do not depend on the workers themselves but are mainly achieved by the nest structure: a
stimulating configuration triggers the response of a termite worker, transforming the configuration into
another configuration that may trigger in turn another (possibly different) action performed by the same
termite or any other worker in the colony. Another illustration of how stimergy and self-organization can be
combined into more subtle adaptive behaviors is recruitment in social insects. Self-organized trail laying by
individual ants is a way of modifying the environment to communicate with nest mates that follow such
trails. It appears that task performance by some workers decreases the need for more task performance: for
instance, nest cleaning by some workers reduces the need for nest cleaning [10,9]. Therefore, nest mates
communicate to other nest mates by modifying the environment (cleaning the nest), and nest mates respond
to the modified environment (by not engaging in nest cleaning); that is stigmergy. Division of labor is
another paradigmatic phenomenon of stigmergy. But by far more crucial to the present work and aim, is how
ants form piles of items such as dead bodies (corpses), larvae, or grains of sand (fig.1a, section 3.1). There
again, stigmergy is at work: ants deposit items at initially random locations. When other ants perceive
deposited items, they are stimulated to deposit items next to them, being this type of cemetery clustering
organization and brood sorting a type of self-organization and adaptive behavior.
There are other types of examples (e.g. prey collectively transport), yet stigmergy is also present: ants
change the perceived environment of other ants (their cognitive map, according to [6,28]), and in every
example, the environment serves as medium of communication. What all these examples have in common is
that they show how stigmergy can easily be made operational. As mentioned by Bonabeau et al. [10], that is
a promising first step to design groups of artificial agents which solve problems: replacing coordination (and
possible some hierarchy) through direct communications by indirect interactions is appealing if one wishes to
design simple agents and reduce communication among agents. Finally, stigmergy is often associated with
flexibility: when the environment changes because of an external perturbation, the insects respond
appropriately to that perturbation, as if it were a modification of the environment caused by the colony’s
activities. In other words, the colony can collectively respond to the perturbation with individuals exhibiting
the same behavior. When it comes to artificial agents, this type of flexibility is priceless: it means that the
agents can respond to a perturbation without being reprogrammed to deal with that particular instability. In
our context, this means that no classifier re-training is needed for any new sets of data-item types (new
classes) arriving to the system, as is necessary in many classical models, or even in some recent ones.
Moreover, the data-items that were used for supervised purposes in early stages in the colony evolution in his
exploration of the search-space, can now, along with new items, be re-arranged in more optimal ways.
Classification and/or data retrieval remains the same, but the system organizes itself in order to deal with new
classes, or even new sub-classes. This task can be performed in real time, and in robust ways due to system’s
redundancy, as was shown in [23,2].
On the present work it is our intention to show that a new type of Data-Mining can be designed based on
Stigmergic paradigms, taking profit of the above cited different natural features, incorporating them on the
intelligent processing. By hybridizing Swarm Intelligence with Evolutionary Computation we seek for an
entire distributed, adaptive, collective and cooperative self-organized Data-Mining. As a real-world / real-
time test bed of our proposal, World-Wide-Web Mining will be used.
2. STIMERGY, SELF-ORGANIZATION AND DATA-MINING
Data clustering is also one of those problems in which real ants can suggest very interesting heuristics for
computer scientists. One of the first studies using the metaphor of ant colonies related to the above clustering
domain is due to Deneubourg [8], where a population of ant-like agents randomly moving onto a 2D grid are
allowed to move basic objects so as to cluster them. This method was then further generalized by Lumer and
Faieta [18] (here after LF algorithm), applying it to exploratory data analysis, for the first time. In 1995, the
two authors were then beyond the simple example, and applied their algorithm to interactive exploratory
database analysis, where a human observer can probe the contents of each represented point (sample, image,
item) and alter the characteristics of the clusters. They showed that their model provides a way of exploring
complex information spaces, such as document or relational databases, because it allows information access
based on exploration from various perspectives. However, this last work entitled “Exploratory Database