Natural Computation for Business Intelligence from Web Usage Mining
Ajith Abraham
IITA Professorship Program, School of Computer Science and Engineering,
Chung-Ang University, Korea, ajith.abraham@ieee.org
Abstract

2. Natural Computation and its Ingredients

Web usage mining attempts to discover useful knowledge
from the secondary data obtained from the interactions of
the users with the Web. Web usage mining has become
very critical for effective Web site management, creating
adaptive Web sites, business and support services,personalization, network traffic flow analysis and so on.
This paper introduces the various ingredients of natural
computation and further presents the important conceptsof Web usage mining and its various practicalapplications.

Natural Computation is a general term referring tocomputing inspired by nature. The idea is to mimic thecomplex phenomena occurring in the nature ascomputational processes in order to enhance the way
computation is performed mainly from a problem solving
point of view. Characteristic for man-designed computing
inspired by nature is the metaphorical use of concepts,
principles and mechanisms underlying natural systems.
The general area of computational intelligence is currently
undergoing an important transformation by trying toincorporate computational ideas borrowed from the nature
all around us:

1. Introduction
The WWW continues to grow at an amazing rate as an
information gateway and as a medium for conducting
business. Web mining is the extraction of interesting anduseful knowledge and implicit information from artifacts
or activity related to the WWW. Based on several
research studies we can broadly classify web mining into
three domains: content, structure and usage mining. Web
content mining is the process of extracting knowledge
from the content of the actual web documents (textcontent, multimedia etc.). Web structure mining istargeting useful knowledge from the web structure,
hyperlink references and so on. Web usage mining
attempts to discover useful knowledge from the secondary
data obtained from the interactions of the users with the
web [7]. Web usage mining has become very critical for
effective web site management, creating adaptive web
sites, business and support services, personalization andnetwork traffic flow analysis [16][18][19].
Natural computation involves ideas/themes
inspired from the nature including biological, ecologicaland physical systems. It is an emerging interdisciplinary
area in which a range of techniques and methods arestudied for dealing with large, complex, and dynamic
problems.
Natural computation and its key paradigms are
introduced in Section 2. In Section 3, web usage mining
related research is summarized. Further in Section 4 aframework for web usage mining is presented and someconclusions are also provided towards the end.

• Artificial immune system• Swarm intelligence• Evolutionary computation• Neural computation• Bacterial foraging• DNA computation• Computing with words• Simulated annealing• Cultural algorithms• Membrane computing• Quantum computation• Artificial life
• Hybrid approaches
2.1 Artificial Neural Networks
Artificial neural networks have been developed as
generalizations of mathematical models of biologicalnervous systems. In a simplified mathematical model ofthe neuron, the effects of the synapses are represented byweights that modulate the effect of the associated input
signals, and the nonlinear characteristic exhibited byneurons is represented by a transfer function, which isusually the sigmoid, Gaussian function etc. The neuron
impulse is then computed as the weighted sum of the input

signals, transformed by the transfer function. The learning
capability of an artificial neuron is achieved by adjusting
the weights in accordance to the chosen learningalgorithm. The learning situations in neural networks may
be classified into three distinct sorts. These are supervised
learning, unsupervised learning and reinforcement
learning [28].
2.2. Evolutionary Algorithms (EA)
Evolutionary algorithms are adaptive methods, which may
be used to solve search and optimization problems, basedon the genetic processes of biological organisms [29].
Over many generations, natural populations evolve
according to the principles of natural selection and‘survival of the fittest’. By mimicking this process,evolutionary algorithms are able to ‘evolve’ solutions toreal world problems, if they have been suitably encoded.
Usually grouped under the term evolutionary algorithms
or evolutionary computation, we find the domains ofgenetic algorithms, evolution strategies, evolutionary
programming, genetic programming and learningclassifier systems. They all share a common conceptualbase of simulating the evolution of individual structures
via processes of selection, mutation, and reproduction.
2.3. Swarm Intelligence
Swarm intelligence is aimed at collective behaviour of
intelligent agents in decentralized systems. Most of thebasic ideas are derived from the real swarms in the nature,
which includes ant colonies, bird flocking, honeybees,
bacteria and microorganisms etc. Swarm models are
population-based and the population is initialised with apopulation of potential solutions [30]. These individuals
are then manipulated (optimised) over many several
iterations using several heuristics inspired from the social
behaviour of insects in an effort to find the optimal
solution. Ant Colony Optimization (ACO) algorithms are
inspired by the behavior of natural ant colonies, in thesense that they solve their problems by multi agent
cooperation using indirect communication throughmodifications in the environment. Ants release a certain
amount of pheromone (hormone) while walking, and eachant prefers (probabilistically) to follow a direction, which
is rich of pheromone. This simple behavior explains why
ants are able to adjust to changes in the environment, suchas optimizing shortest path to a food source or a nest. In
ACO, ants use information collected during past
simulations to direct their search and this information isavailable and modified through the environment.
2.4. Artificial Immune System (AIS)The artificial immune systems like other biologicallyinspired techniques, tries to extract ideas from a natural
system, in particular the vertebrate immune system, inorder to develop computational tools for solving
engineering problems. The basic idea of AIS is to exploit
the immune system's characteristics of learning andmemory to solve a problem. AIS can be broadly
categorized into three subgroups: those using the clonal
selection theory, those using negative selection and thoseusing the immune network theory as their main inspiration[32].
2.5. Bacterial Foraging
Selection behavior of bacteria tends to eliminate animals
with poor foraging strategies and favor the propagation ofgenes of those animals that have successful foraging
strategies. After many generations, a foraging animal takes
actions to maximize the energy obtained per unit timespent foraging [31]. That is, poor foraging strategies areeither eliminated or shaped into good ones. Escherichia
coli (E. coli) is a common type of bacteria whose behavior
to move comes from a set of up to six rigid 100–200 rps
spinning flagella, each driven as a biological motor. An E.
coli bacterium alternates between running and tumbling.
The chemotactic actions of the bacteria are summarized asfollows:• If in a neutral medium, alternate tumbles and runs, itsaction is like performing a search.
• If swimming up a nutrient gradient (or out of noxious
substances), or swim longer (climb up nutrient gradientor down noxious gradient) its behavior seeks
increasingly favorable environments.
• If swimming down a nutrient gradient (or up noxious
substance gradient), then search action is to avoid
unfavorable environments.
2.6. DNA Computation
DNA computing is a form of computing which uses DNA(Deoxyribo-Nucleic Acid) and molecular biology, insteadof the traditional silicon-based microprocessors. Just likea string of binary data is encoded with ones and zeros, astrand of DNA is encoded with four bases, represented by
the letters A, T, C and G (nucleotides) and the data
density is very impressive. An important property of DNA
is its double stranded nature with every DNA sequence
having a natural complement. This complementarity
feature makes DNA a unique data structure forcomputation and can be exploited in many ways. In thecell, DNA is modified biochemically (on the molecularlevel) by a variety of enzymes, which are tiny protein
machines. This mechanism along with some synthetic
chemistry is responsible for the various DNA computation
operators [33].
2.7. Computing with Words
Computing with words is a methodology in which theobjects of computation are words and propositions drawn

from a natural language. Computing with words is inspiredby the brain's crucial ability to manipulate perceptions
without any measurements or computations. Computing
with words provides a foundation for a computational
theory of perceptions. A basic difference between
perceptions and measurements is that, in general,measurements are crisp, whereas perceptions are fuzzy[34].
2.8. Simulated Annealing
Simulated annealing is based on the manner in which
liquids freeze or metals re-crystalize in the process ofannealing [20]. In an annealing process, molten metal,
initially at high temperature, is slowly cooled so that thesystem at any time is approximately in thermodynamic
equilibrium. If the initial temperature of the system is too
low or cooling is done insufficiently slowly the system
may become brittle or unstable with forming defects. The
initial state of a thermodynamic system is set at energy E
and temperature T, holding T constant the initialconfiguration is perturbed and the change in energy dE iscomputed. If the change in energy is negative the new
configuration is accepted. If the change in energy ispositive it is accepted with a probability given by the
Boltzmann factor exp -(dE/T). This processes is thenrepeated for few iterations to give good sampling statistics
for the current temperature, and then the temperature isdecremented and the entire process repeated until a frozen
state is achieved at T=0.
2.9. Cultural Algorithms
Cultural Algorithms are computational models of cultural
evolution. They consist of two basic components, a
population space (using evolutionary algorithms), and abelief space. The two components interact by means of avote-inherit-promote protocol [35]. Likewise theknowledge acquired by the problem solving activities ofthe population can be stored in the belief space in the formof production rules etc. Cultural algorithms represent a
general framework for producing hybrid evolutionary
systems that integrate evolutionary search and domain
knowledge.
2.10. Artificial Life
Artificial life (alife) attempts at setting up systems withlife like properties which all biological organisms possess,such as reproduction, homeostasis, adaptability etc. Alife
is often described as attempting to understand high-level
behavior from low-level rules; for example, how the
simple rules of Darwinian evolution lead to high-levelstructure, or the way in which the simple interactions
between ants and their environment lead to complex trailfollowing behavior [36]. Understanding this relationshipin particular systems promises to provide novel solutions

to complex real-world problems, such as disease
prevention, stock-market prediction, and data-mining on
the Internet.
2.11. Membrane Computing
Membrane computing (P systems) is a framework which
abstracts from the way live cells process chemicalcompounds in their compartmental structure [21]. In amembrane system multisets of objects are placed in thecompartments defined by the membrane structure, and theobjects evolve by means of reaction rules are also associatedwithin the compartments, and applied in a maximallyparallel, nondeterministic manner. The objects can be
described by symbols or by strings of symbols. For
symbol-objects, a set of numbers are computed, and in thecase of string-objects a set of strings are computed which ismore like a language. The objects are able to throughmembranes and the membranes can dissolve, divide andchange their permeability. These features are used indefining transitions between configurations of the system,and sequences of transitions are used to define computations.
A sequence of transitions is a computation.
2.12. Quantum Computation
In conventional silicon computers, the amount of data ismeasured by bits; in a quantum computer, it is measured
by qubits (quantum-bit). A qubit can be a 1 or a 0, or it
can exist in a superposition that is simultaneously both 1
and 0 or somewhere in between. The basic principle ofquantum computation is that the quantum properties ofparticles can be used to represent and structure data, andthat devised quantum mechanisms can be used to perform
operations with this data [22].
2.13. Hybrid Approaches
Several adaptive hybrid intelligent systems have in recent
years been developed and many of these approaches use
the combination of different knowledge representation
schemes, decision making models and learning strategies
to solve a computational task [23]. This integration aims
at overcoming limitations of individual techniques throughhybridization or fusion of various techniques. It is wellknown that the intelligent systems, which can provide
human like expertise such as domain knowledge,
uncertain reasoning, and adaptation to a noisy and timevarying environment, are important in tackling practicalcomputing problems. In contrast with conventional
artificial intelligence techniques which only deal with
precision and certainty the guiding principle of hybrid
approaches is to exploit the tolerance for imprecision,
uncertainty, robustness and to provide optimal solutionsetc.

Web structure/content
information

Business services

Pattern analysis

Pattern discovery

Raw Web log data

Data pre-processing
and cleaning

Web server

Business intelligence
Usage statistics—

ISP

Sequential/association
rule mining

Customer / Client

Figure 1. Web usage mining framework

3. Web Usage Mining and BusinessIntelligence
The rapid e-commerce growth has made both business
community and customers face a new situation. Due tointense competition on the one hand and the customer's
option to choose from several alternatives, the business
community has realized the necessity of intelligent
marketing strategies and relationship management.
Web servers record and accumulate data about userinteractions whenever requests for resources are received.
Analyzing the Web access logs can help understand theuser behavior and the web structure. From the businessand applications point of view, knowledge obtained fromthe web usage patterns could be directly applied toefficiently manage activities related to e-business, eservices and e-education. Accurate web usage information
could help to attract new customers, retain current
customers, improve cross marketing/sales, effectiveness ofpromotional campaigns, tracking leaving customers etc.
The usage information can be exploited to improve theperformance of Web servers by developing proper prefetching and caching strategies so as to reduce the server
response time. User profiles could be built by combining

users’ navigation paths with other data features, such as
page viewing time, hyperlink structure, and page content[12].
What makes the discovered knowledge interesting had
been addressed by several works. Results previously
known are very often considered as not interesting. So the
key concept to make the discovered knowledge interesting
will be its novelty or unexpected appearance.
Web Usage Mining techniques can be used to anticipate
the user behavior in real time by comparing the current
navigation pattern with typical patterns which wereextracted from past Web log. Recommendation systems
could be developed to recommend interesting links to
products which could be interesting to users.
One of the major issues in web log mining is to groupall the users’ page requests so to clearly identify the paths
that users followed during navigation through the website. The most common approach is to use cookies to trackdown the sequence of users’ page requests or by using
some heuristic methods. Session reconstruction is alsodifficult from proxy server log file data and sometimes not
all users’ navigation paths can be identified.

Figure 2. Web usage mining framework using natural computation [3]Users’ behavior can also be tracked down on the serverside by means of TCP/IP packet sniffers.
Figure 1 [1] illustrates the basic Web usage mining
framework. Whenever a visitor accesses the server it
leaves, e.g., the IP, authenticated user ID, time/date,request mode, status, bytes, referrer and agent. The
available data fields are specified by the HTTP protocol.
There are several commercial software that could provide
web usage statistics. These statistics could be useful for
web administrators to get a sense of the actual load on theserver. For small web servers, the usage statistics provided
by conventional web site trackers may be adequate to
analyze the usage patterns and trends. However, as thesize and complexity of the data increases, the statistics
provided by existing web log file analysis tools alone may
prove inadequate and more knowledge mining andintelligent information processing will be necessary
3.1. Data Sources
The usage data collected at different sources represent thenavigation patterns of different segments of the overall
web traffic, ranging from single user, single site browsing
behavior to multi-user, multi-site access patterns. Web
server log does not accurately contain sufficient
information for inferring the behavior at the client side as
they relate to the pages served by the web server. Data
may be collected from (a) Web servers, (b) proxy servers,and (c) Web clients. Web servers collect large amounts of
information in their log files.

Databases are used instead of simple log files to store
information so to improve querying of massive log
repositories [24]. Internet service providers use proxy
server services to improve navigation speed throughcaching. Collecting navigation data at the proxy level isbasically the same as collecting data at the server level but
the proxy servers collects data of groups of users
accessing groups of web servers. Usage data can betracked also on the client side by using Javascript, Java
applets, or even modified browsers [25].
3.2. Data Pre-Processing
The raw web log data after pre-processing and cleaning
could be used for pattern discovery, pattern analysis, web
usage statistics, and generating association/ sequential
rules. Much work has been performed on extracting
various pattern information from web logs and theapplication of the discovered knowledge range from
improving the design and structure of a web site toenabling business organizations to function more
efficiently.
Data pre-processing involves mundane tasks such as
merging multiple server logs into a central location andparsing the log into data fields. The preprocessing
comprises of (a) the data cleaning, (b) the identificationand the reconstruction of users’ sessions, and (c) the data
formatting [26].
Data cleaning Consists of removing all the data tracked
in Web logs that are useless for mining purposes. Graphic
file requests, agent/spider crawling etc. could be easilyremoved by only looking for HTML file requests.
Normalization of URL’s is often required to make therequests consistent. For example requests forwww.okstate.edu
orwww.okstate.edu/index.html
are all for the same file.
Session identification and reconstruction consists ofidentifying the different users’ sessions from the usually
very poor information available in log files andreconstructing the users’ navigation path within theidentified sessions. Most of the problems encountered inthis phase are caused by the caching performed either by
proxy servers either by browsers. Data formatting is the
final step of preprocessing in which data are properly
formatted before applying data mining techniques.
Most of the Web usage mining applications research
effort focuses on three main paradigms: association rules,
sequential patterns, clustering and prediction [11].
Association rules are used to find associations among
Web pages that frequently appear together in users’
sessions. Sequential pattern discovery exposes sequential
navigation patterns that appear in users’ sessions
frequently. Clustering has been widely used to group
together similar sessions/access patterns. Prediction
methods are used to forecast the volume of visitors /number of server accesses etc.
Jespersen et al. [9] proposed an hybrid approach foranalyzing the visitor click sequences. A combination ofHypertext Probabilistic Grammar (HPG) and click fact
table approach is used to mine web logs, which could bealso used for general sequence mining tasks. In HPG, anon-terminal symbol corresponds to a web page and a
production rule corresponds to a hypertext link.
Mobasher et al. [13] proposed the web personalization
system that consists of offline tasks related to the miningof usage data and online process of automatic web pagecustomization based on the knowledge discovered.
LOGSOM proposed by Smith et al. [17], utilizes selforganizing map to organize web pages into a twodimensional map based solely on the users’ navigation
behavior, rather than the content of the web pages.
LumberJack proposed by Chi et al. [6] builds up user
profiles by combining both user session clustering andtraditional statistical traffic analysis using K-means
algorithm.
Joshi et al. [10] used relational online analyticalprocessing approach for creating a web log warehouseusing access logs and mined logs (association rules andclusters). Holden and Freitas [27] used an ant colonyalgorithm for Web page classification. A comprehensive
overview of web usage mining research is found in[26][7].

In Figure 2, we present a web usage-mining frameworkusing natural computing techniques [1]. Clustering of thelog data helps to segregate similar visitors and thefunction approximation algorithm takes care of analyzing
the visitor patterns and developing some trends. We
started this research using a simple architecture consistingof a pre-processing block followed by self-organizing map(clustering) and neural network (function approximation).
To improve the performance, we further investigatedvarious hybrid combinations to cluster similar visitors
based on the domain of origin, no of pages requested, timeof access etc. For data clustering, self-organizing maps,
evolutionary fuzzy clustering algorithm [2] and ant colony
clustering algorithm [3] were used. The clustered data isthen used to analyze the trends using several function
approximation algorithms like neural networks; TakagiSugeno fuzzy inference system trained using neuralnetwork learning [4]; and linear genetic programming [5].
To demonstrate the efficiency of the proposed
frameworks, web access log data at the Monash
University’s web site [14] were used for experimentations.
Readers are advised to consult [1][2] and [3] for further
details about the experiments and results.

4. Conclusions
This paper introduced the various ingredients and basic
ideas of some of the important natural computation
techniques. Further the importance of web usage mining
in business intelligence was presented with an emphasison data sources, data pre-processing and some related
research.

Acknowledgements
This research was supported by the International Joint
Research Grant of the IITA (Institute of Information
Technology Assessment) foreign professor invitation
program of the MIC (Ministry of Information andCommunication), Korea.

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