1
Editors – Prof. Amos DAVID & Prof. Charles UWADIA
Arts
Technology
Faith Science
Transition from Observation to Knowledge to
Intelligence
25-26 August 2016
University of Lagos, Nigeria
Editors
Prof. Amos DAVID
Prof. Charles UWADIA
87
Actionable Knowledge Discovery: The Analytics
Hierarchy Process Approach
Ikuvwerha L.O., Odumuyiwa, V.T, Ogunbiyi T.D, Uwadia, C.O.,
Abass O.
Ikuvwerha L.O., Odumuyiwa, V.T, Ogunbiyi T.D, Uwadia,
C.O., Abass O.
Department of Computer Sciences,
University of Lagos, Nigeria Abstract: The traditional data mining algorithms and tools stop at mining data that
provide patterns that satisfy the expected technical interestingness. Meanwhile business
people finds it difficult to apply the mined patterns in taking necessary actions to
support their business needs. Therefore, there is the need to move from ordinarily
mining of patterns to discovering actionable patterns (knowledge). One of the major
issue in actionable knowledge is interestingness measure. Interestingness measure can
be generally divided into two categories: objective measure which is based on statistical
strength of the data mining method and the subjective measure which is based on the
user’s beliefs or expectations of the particular problem domain. This research focus is
on subjective measure of interestingness. Analytic Hierarchy Process (AHP) approach
is used to measure the subjective interestingness of the patterns. The three subjective
measure criteria that were used to formulate the AHP model are pattern actionability,
expectedness and novelty. This research uses an illustrative example to demonstrate the
concept of this model. The criteria/threshold in the algorithm of data mining stage is
used as the objective measure while the AHP model is used as the subjective measure
to evaluate and rank the pattern according to their actionability that is its usability. This
research shows that this model is more effective because it considers both the technical
interestingness and the business interestingness. This research also shows that AHP can
be effectively used as subjective interestingness which is the business interestingness.
Keywords: Actionable patters, AHP, interestingness
Actionable Knowledge Discovery: The Analytics Hierarchy Process Approach
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1. Introduction
One of the central problems of data mining is the discovering of
interestingness and actionable patterns. “Actionable patterns” is
referred to knowledge that end-user (which could be decision –maker)
can act upon or take action on. Most data mining algorithms and tools
stop at the mining and delivery of patterns satisfying expected technical
interestingness. There are often many patterns mined but business
people either are not interested in them or do not know what follow-up
actions to take to support their business decisions. Therefore, it is
important to filter these patterns through the use of some measures
(interestingness) to produce patterns that are actionable and usable to
the end-users. It is very important to understand the overall approach
before one attempts to extract useful knowledge from data and define
good measures of interestingness that would allow the entire process or
system to discover only the useful patterns. Interestingness measure can
be generally divided into two categories: objective measure which is
based on the strength of the statistical method of the data mining criteria
and subject measure based on the user’s beliefs or expectations of the
particular problem domain. The Analytic Hierarchy Process (AHP)
developed by Thomas Saaty for decision making uses a well-defined
mathematical structure of consistent metrics called Pair-wise
comparison matrix to process the inescapably subjective and personal
preferences of an individual or a group in making a decision. With the
use of AHP, it is easier to construct hierarchies or feedback networks,
then a Pair-wise comparison matrix of elements with respect to a
controlling factor is used to derive ratio scales that are then synthesized
throughout the structure to select the best alternative (saty,2008).
Actionable knowledge discovery is selected as one of the greatest
challenges of next-generation knowledge discovery in database (KDD)
studies (Ankerst, 2002; Fayyad, Shapiro, 2003). Mined patterns are
often non-actionable in the existing data mining and knowledge
discovery in database to real user needs. According to Cao & Zhang
(2007) “the traditional data-centered mining methodology could be
complimented by the involvement of domain-related social intelligence
in data mining which leads to domain-driven data mining “Simply
Ikuvwerha L.O., Odumuyiwa, V.T, Ogunbiyi T.D, Uwadia, C.O., Abass O
89
knowing many algorithms used for data analysis is not sufficient for a
successful data mining (DM) and Knowledge Discovery (KD) project.
The major research questions are: how can data and knowledge
satisfying business users’ request be presented; what is generic business
interestingness framework; and how can this framework produce
reliable and trustworthy knowledge. This paper is organized as follow:
section 2 is the related works, section 3 discusses the AHP model,
section 4 look at an illustrative example and section 5 is the conclusion.
2. Related works
Data mining and knowledge discovery is a process that must involve
the user at every stage from the initial stage to the final stage of analysis.
According to Mcgarry (2005) the taxonomy of interestingness
measures is shown in figure 1
Figure1: Measure of interestingness
Dharanik and Gudikandula (2012) used multi-step mining to
discover actionable knowledge. This proposed framework combines
multiple data source, multiple methods and multiple features to obtain
actionable patterns set using existing algorithms. The model could be
used in the banking sector, insurance etc. The limitations of this model
or framework are; the matrix used to determine the actionability of the
patterns in the specified domain is not well defined and the domain
environment and constraints are not considered.
Cao (2012), summarized the extreme imbalances that exist in the
current data mining, which are:
Algorithm imbalance
Pattern Imbalance
Decision Imbalance
Measure of interestingness
Objective
Coverage Support accuracy
Subjective
Unexpected Actionable Accuracy
Actionable Knowledge Discovery: The Analytics Hierarchy Process Approach
90
The paper treats AKD as closed optimization problem.
AKD:=OPTIMAZATION (PROBLEM, DATA, ENVIRONMENT,
MODEL, DECISION)
AKD is a problem-solving Process that transforms business problem
ѱ with problem status t to a problem- solution ф.
Ѱ(./t) ф( ). …….. (1)
Kavitha and Ramaraj (2013), presented a framework that uses
combined mining and composite approach to generate actionable
patterns in terms of rules. The concept from meta- learning that uses
decision theory was used to formulate a utility interestingness measures
(objective and subjective). Zoo and Mushroom data from the University
of California Irvine was used for the experiment.
Amruta and Balachandran (2013), reviewed the four most used
AKD frameworks for business need. These frameworks are:
Postanalysis-interestingness-based AKD
Unified-interestingness-based AKD
Combined-mining-based AKD
Multisource combined- mining- based AKD
Their performance (the numbers of actionable pattern sets) was
evaluated under decision making system using a real time tennis data
set. The multisource combine-mining-based AKD performs better than
the others.
2.1. What is Actionable Knowledge?
The term “actionable pattern” refers to knowledge that can be
uncovered in large complex databases and can act as the impetus for
some action. It is important to distinguish these actionable patterns from
the lower value patterns that can be found in great quantities and with
relative ease through so called data dredging. This highlights the need
to make optimal use of the human in the KDD process for directing the
exploration and evaluating its results — one cannot simply apply some
Ikuvwerha L.O., Odumuyiwa, V.T, Ogunbiyi T.D, Uwadia, C.O., Abass O
91
predefined procedure to distill large volumes of data into high value
knowledge. Rather, KDD tools must provide users with the insight
required to focus the tool’s search processes and with the means to
efficiently evaluate discovered patterns.
Discovering actionable knowledge has been viewed as the essence
of KDD. However, even up to now, it is still one of the great challenges
to existing and future KDD as pointed out by the panel of SIGKDD
2002 and 2003 (Cao & Zhang 2007a, 2007b). This situation partly
results from the limitation of traditional data mining methodologies,
which view KDD as a data-driven trial-and-error process targeting
automated hidden knowledge discovery (Cao & Zhang 2007a, 2007b).
The traditional data mining methodologies do not take into much
consideration the constrained and dynamic environment of KDD; these
methodologies naturally excludes humans and problem domain in the
loop. As a result, very often data mining research mainly aims to
develop, demonstrate, and push the use of specific algorithms while it
runs off the rails in producing actionable knowledge of main interest to
specific user needs.
2.3.1 Measuring Knowledge Actionability
Often mined patterns are non-actionable to real needs due to the
interestingness gaps between academia focused-interestingness
(objective interestingness) and business focused-interestingness
(subjective interestingness). Measuring actionability of knowledge is to
recognise statistically interesting patterns permitting users to react to
them to better service business objectives. The measurement of
knowledge actionability should be from perspectives of both objective
and subjective interestingness.
Definition 1. Actionability of a pattern: Given a pattern P, its
actionable capability act() is described as to what degree it can satisfy
both technical interestingness and business one.
If a pattern is automatically discovered by a data mining model if it
only satisfies technical interestingness request, it is usually called an
(technically) interesting pattern. It is presented as
∀ x ∈ I, ∃P : x.tech_int(P) ∧ x.act(P)
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In a special case, if either technical and business interestingness, or
hybrid interestingness measure integrating both aspects, are satisfied, it
is called an actionable pattern. It is not only interesting to data miners,
but generally interesting to decision-makers.
∀ x ∈ I, ∃P : x.tech_int(P) ∧ x.biz_int(P) ∧ x.act(P)
Therefore, the work of actionable knowledge discovery must focus
on knowledge findings, which can not only satisfy technical
interestingness but also business measures.
The real requirements for discovering actionable knowledge in
constraint-based context imply that real data mining is more likely to
be human involved rather than automated. Human involvement is
embodied through the cooperation between humans (including users
and business analysts, mainly domain experts) and data mining system.
This is achieved through the complementation between human
qualitative intelligence such as domain knowledge and field
supervision, and mining quantitative intelligence like computational
capability. Therefore, real-world data mining involves a human-
machine- collaborative knowledge discovery process.
3. Comparison Between Knowledge Discovery Process (KDP)
And Actionable Knowledge Discovery (AKD)
The basic differences between KDP and AKD are show in table 1.
These differences are described from deferent aspects as illustrated
below.
ASPECTS KDP AKD (DOMAIN – DRIVEN)
OBJECT
MINED
Data tells the story Data and Domain tells the story
AIM Develop innovative
approach
Generate business impacts
OBJECTIVE Algorithms are the focus Solving business problem is the
focus
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DATA SET Mining abstract and refined
data set
Mining constraints real- life data
PROCESS Data mining is an
automated process
Humans are integrated into the
process
EVALUATION Based on technical metrics Based on actionable options
GOAL Let data create and verify
research innovation. Push
novel algorithms to
discover knowledge of
research interest.
Let data and metasynthetic
knowledge tell the hidden
business story. Discover
actionable knowledge to satisfy
end user
TABLE 1: COMPARISONS OF KDP AND AKD (DOMAIN-DRIVEN MINING).
4. The Analytic Hierarchy Process (AHP)
The foundation of the Analytic Hierarchy Process (AHP) is a set of
axioms that carefully delimits the scope of the problem environment
(Saaty 1996). It is based on the well-defined mathematical structure of
consistent matrices and their associated right- eigenvector's ability to
generate true or approximate weights, (Saaty,1980). The AHP
methodology compares criteria, or alternatives with respect to a
criterion, in a natural, pair wise mode. To do so, the AHP uses a
fundamental scale of absolute numbers that has been proven in practice
and validated by physical and decision problem experiments. The
fundamental scale has been shown to be a scale that captures individual
preferences with respect to quantitative and qualitative attributes just as
well or better than other scales (Saaty 1980). It converts individual
preferences into ratio scale weights that can be combined into a linear
additive weight w(a) for each alternative a. The resultant w(a) can be
used to compare and rank the alternatives and, hence, assist the decision
maker in making a choice. The basic steps in AHP are reasonable
descriptors of how an individual comes naturally to resolving a multi
criteria decision problem, then the AHP can be considered to be both a
descriptive and prescriptive model of decision making. The AHP is
perhaps, the most widely used decision making approach in the world
today. Its validity is based on the many hundreds (now thousands) of
Actionable Knowledge Discovery: The Analytics Hierarchy Process Approach
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actual applications in which the AHP results were accepted and used by
the cognizant Decision Makers, Saaty (1996).
An example of such a hierarchy is presented in Figure 2. At the top
level, a goal is specified, in this case sustainable catchment use. At the
second level, all the objectives or criteria are listed, which in this
example are environmental, economic and social objectives. At the
bottom level, all the decision options are presented
Figure 2: Example of an AHP structure
Criteria or objectives can be divided into sub- or sub-sub-criteria
(objectives) for additional information and for clarification and
refinement. Criteria can be subjective (such as impact of trees on
recreational values) or objective (such as tree planting cost), depending
on the means used in evaluating the contribution of those criteria below
them in the hierarchy. Criteria are regarded as mutually exclusive and
do not depend on the elements below them in the hierarchy.
5. PROPOSED CONCEPTUAL MODEL.
The proposed model is called Analytic Hierarchy Process –
Actionable knowledge Discovery (AHP-AKD). The proposed
conceptual model is developed based on the six-step knowledge
Discovery process model (Pal, and Jain 2005). Apart from the first step,
the other five steps are grouped into three tiers which are Data tier, Data
Mining Tiers and Knowledge Discovery Tier. This is shown in Figure
4.
Ikuvwerha L.O., Odumuyiwa, V.T, Ogunbiyi T.D, Uwadia, C.O., Abass O
95
5.1. THE PROPOSED MODEL FRAMEWORK.
We considered the six-step knowledge discovery process to develop the
proposed model (Hybrid model). The description of the proposed model
is as follow:
STEP 1: Understanding the Problem Domain: it is at this step we define
the problems that needed to be solved.
STEP 2: AHP to formulate the problem structure: The problem
structure is formulated by employing AHP model.
STEP 3: Understanding of the data: this step involves the collection of
sample data and deciding which data is needed to achieve the project
goals and to solve the problem in step 1. Data are checked for missing
value, redundancy, completeness etc.
STEP 4: Preparation of the data: this is the step where we select the
data that is to be used for the Data mining methods or algorithms.
STEP 5: Data Mining: it is at this stage we apply various data mining
methods and tools. The methods or algorithms that will be used at this
Knowledge Discovery Tier
Data Mining Tier
Data Tier
Understanding of the
problem
Understanding of the
Data
Preparation of the
Data
DATA MINING
PROCESS
Evaluation of the
Discovered knowledge
Use of the
Knowledge
Discovered
Model :
is
knowle
dge
actiona
ble
Knowledge
patterns,
rules, clusters,
etc
Data
Source
Knowledge
Base
Extend Knowledge
to other Domain
N
o
Y
e
s
AHP TO
FORMULATE
DOMAIN
PROBLEM
Actionable Knowledge Discovery: The Analytics Hierarchy Process Approach
96
step are based on the identified project goals that are translated into data
mining goals in step 1.
Figure 4: THE PROPOSED CONCEPTUAL MODEL AHP-AKD.
STEP 6: Evaluation of the discovered Knowledge: the discovered
knowledge (either in form of patterns, rules, clusters etc) is checked to
see whether they are novel, interesting and actionable. Domain experts
are involved in the interpretation of the results.
STEP 7: Use of discovered Knowledge: this is the final step where the
discovered knowledge is used by the end user or decision maker to plan
and take action.
This proposed model has several feedback mechanisms. The entire
process is iterative. The expected iterative feedback loops are:
1. From step 3 to step1: This loop will help us to have a better
understanding between the data base and the problem domain.
2. From step 3 to step 2: A proper and further understanding of
the data will enable us to know the best preprocessing
algorithms to use.
3. From step 4 to step1: when the data mining process generates
unsatisfactory result, the process could go back to step1 to see
if the project goals should be modified or the problem is not
well understood.
4. From step 4 to step 3: this loop is necessary because some data
mining methods require us to prepare the data in such a way
that it could fit into the method. This is not known early in step
3 because we don’t know the data mining method or tool that
will be used.
5. From step 5 to step 1: The model we propose to develop in step
5 will check for the actionability, novelty, and interestingness
of the knowledge discovered. If the discovered knowledge is
not actionable, this could be as a result of incorrect
understanding of the problem domain, requirement and goals.
The process goes back to step 1.
Ikuvwerha L.O., Odumuyiwa, V.T, Ogunbiyi T.D, Uwadia, C.O., Abass O
97
6. Illustrative Example/Experimental Result
According to mcgarry (2005), a data mining algorithm produced the
following patterns.
Patterns 1: IF (age > 60) ∧ (salary = high) THEN
loan =approved
Patterns 2: IF (age < 60) ∧ (salary = average) ∧ (Record = poor)
THEN
loan = not approved
Patterns 3: IF (age < 60) ∧ (salary = low) THEN
loan = approved
While the end-user/expert defined pattern is
IF (age > 50) ∧(salary = low ) THEN
loan = not approved.
This patterns are the ones that have met with the objective
interestingness measure based on the data mining algorithms. We can
now use AHP to determine the actionability of the pattern produced.
We are proposing AHP as a subjective measure of interestingness.
This problem can be structured using AHP as follow
Fig 5: structured AHP for actionable pattern.
Novel
Actionable Pattern
Actionable Unexpected
Pattern 1 Pattern 3 Pattern 2
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The goal is to find actionable pattern. The criteria used are
actionability, unexpected and novel. The alternatives are Pattern 1,
pattern 2 and pattern 3.
6.1. Pairwise Comparison Judgment Matrices of Model
The pair-wise comparison matrix from all the expert is used to
calculate the criterion weight. A square matrix is used to represent all
such judgments in making comparisons with respect to a single factor
by comparing the set of elements. Each judgment represents the
dominance of an element in the left column of the matrix over an
element in the row on top. In making the final decision, all judgments
with respect to some factors or property to be processed were
synthesized along with other matrices of comparisons involved. The
values of consistency ratio (CR) lower than 0.10 define the matrix A as
acceptable, the values that are slightly higher (between 0.10 and 0.20)
must be considered with care, matrix A should be rejected with higher
values (greater than 0.20).
PAIRWISE MATRIX RESULTS
Table 1: Pairwise comparison matrix with respect to the factors
λmax = 3.041 CR= 0.0356
This result shows that actionable is of more important with 57%,
followed by unexpectedness with 32%, and Novel with 11%. In finding
actionable patterns or knowledge, actionability of the pattern comes
first followed by unexpectedness and novel.
Table 2: Pairwise comparison matrix for the Alternative with
respect actionable factor
FACTORS ACTIONABLE
UNEXPECTED
NOVEL
NORMALISED
EIGEN
VECTOR
ACTIONABLE
1 2 5 0.5701
UNEXPECTED
1/2 1 3 0.3207
NOVEL
1/5 1/3 1 0.1092
Ikuvwerha L.O., Odumuyiwa, V.T, Ogunbiyi T.D, Uwadia, C.O., Abass O
99
λmax = 3.002 CR= 0.0138
the patterns are evaluated according to their actionability. We find
out that pattern 1 is more actionable with 65%, followed by Pattern 2
with 23%, and pattern 3 with 12%.
Table 3: Pairwise comparison matrix for the Alternative with
respect unexpected factor
λmax = 3.0392 CR= 0.033.
This result shows that according to pattern unexpectedness, the
pattern are ranked as follow: pattern 3 with 64.13%, pattern 2 with
23.755 and Pattern 1 with 12.14%. From this it is clear that pattern 3
contradicts the user’s belief and it is therefore unexpected. This also
confirm the result from the Mcgarry (2005) results using
unexpectedness as a factor.
Table 4: Pairwise comparison matrix for the Alternative with
respect Novel factor
ACTIONABL
E
PATTER
N 1
PATTER
N 2
PATTER
N 3
NORMALISE
D EIGEN
VECTOR PATTERN 1
1 5 3 0.6485
PATTERN 2
0.33 1 3 0.2296
PATTERN 3
0.2 0.33 1 0.1219
UNEXPECTED
PATTERN 1
PATTERN 2
PATTERN
3
NORMALISED
EIGEN
VECTOR PATTERN 1
1 0.5 0.2 0.1213
PATTERN 2
2 1 0.33 0.2374
PATTERN 3
5 3 1 0.6413
Actionable Knowledge Discovery: The Analytics Hierarchy Process Approach
100
λmax = 3.038 CR= 0.033
The three factors results are integrated together to form the overall
priority table which is shown in table 5.
Table 5: overall priority
ALTERNATIVES OVREALL PRIORITY
PATTERN 1 0.4664
PATTERN 2 0.2407
PATTERN 3 0.2929
This result shows that the pattern are ranked as follow: pattern 1 with
46.64%, pattern 2 with 24.07% and Pattern 3 with 29.29%. From this it
is clear that pattern 1 is seen to be more actionable followed by pattern
3 and then pattern 2
7. Conclusion
Much of the research in the area of Knowledge Discovery in
Databases (KDD) has focused on the development of more efficient and
effective data mining algorithms. However, recently, issues related to
the usability of these techniques in extracting exploitable knowledge
from databases has drawn significant attention. Real-world data mining
applications for knowledge discovery have proposed urgent requests
NOVEL
PATTERN
1
UNEXPECTED
PATTERN 2
PATTERN
3
NORMALISED EIGEN
VECTOR
PATTERN
1
1 2 3 0.5295
PATTERN
2
0.33 1 2 0.3088
PATTERN
3
0.33 0.5 1 0.1617
Ikuvwerha L.O., Odumuyiwa, V.T, Ogunbiyi T.D, Uwadia, C.O., Abass O
101
for discovering actionable knowledge of main interest to real user and
business needs. The major issue in actionable knowledge discovery is
the interestingness measure: objective and subjective measure. The
proposed conceptual model uses the AHP as the subjective measure and
the data mining Algorithms as the objective measure. It is clear from
table 5 that Pattern 1 is the most actionable pattern based on the end
user’s. It has an average priority of 47% . Pattern 3 is next with an
average priority of 29%. This is then followed by pattern 2 with an
average priority of 24%. This research therefore concludes that AHP
can be effectively used as subjective interestingness measure for
actionable knowledge.
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Transition from Observation to Knowledge to Intelligence (TOKI) Conference is a forum that allows researchers from the fields of Competitive Intelligence, Internet of Things (IoT), Cloud Computing, Big Data and Territorial Intelligence to present their novel research findings and results. Common to all these fields are the concepts of information, information systems, knowledge, intelligence, decision-support systems, ubiquities, etc. The relevance of research findings, results obtained, systems developed and techniques adopted in these research fields for both the industries and government cannot be overemphasized.
Therefore, the Conference welcomes contributions in the following areas:
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Big Data Analytics: This includes Big Data, Information Visualization, Data Analysis and related applications.
Semantic Web: Standardized formats and exchange protocols for web based data.
IoT Analytics: These center around innovative algorithms and data analysis techniques for extracting meaning and value from the Internet of Things.
Resource Management: This includes energy saving techniques, effective and efficient utilization of resources, intelligent data processing, mining, fusion, storage, and management, context awareness and ambient intelligence.
IoT Enabling Technologies: These center around technologies that drive pervasive / ubiquitous systems some of which include but not limited to IPv6, NFC, RFID and Microprocessors.
Interoperable and Adaptive Information Systems: These include but are not limited to Decision Support Systems, collaborative and co-operative systems and other forms of systems that support interfacing of multiple elements and entities.
Mobile IoT: Smart phone applications for generating and consuming data, crowd sourced data, e-commerce, mobile advertising, B2B, B2C and C2C connectedness.
Cloud Computing: Including security, storage and access to data stored in the cloud;
service provisioning and resource utilization; cloud communication protocols;
interoperability among users and devices with respect to linked data.
Editors Prof. Amos DAVID & Prof. Charles UWADIA
9782954676036