Volume Two - Academic Conferences

Cristina Wanzeller and Orlando Belo
Table 2 shows the main extracted and specified values for the example problem description. The
used clickstream data was a server log file, describing data at page view or access level. Concerning
the explicit requisites: (i) for goals we used all the ones regarding to relationships among pages; (ii)
we selected two sub-areas of the “quality of service” application area, since both are relevant and the
closest ones to the intended actions; (iii) we gave the maximum relative importance to Interpretability
evaluation criteria (followed by the other criteria items).
Table 2: Main values for the features of problem description
Problem description: category, features (and values)
(D)
Metadata at
dataset
level:
(D)
Metadata at
variable
level:
(T)
Problem
type
(P)
Evaluation
criteria
Number of lines (7128) and variables (8); granularity (PageView)
% of numeric (0.375), categorical (0.625), temporal (0.125) and binary (0) columns
Type of visitor’s identification (not available) and information recording (not available)
Access order (true) and access repetition availability (true)
Access data and hour availability (true)
Data type: 3 Numeric (1 DateTime; 2 Integer) 5 categorical (String)
Number of distinct values (…) and number of null values (0 for all)
Semantic category (…)
Goals (Discover relationships among pages and items; Determine access order of pages and
items)
Application areas (Impact analysis; Content and structure optimization)
(value; relative importance)
Precision (5; 4); Time of reply (5; 2); Interpretability (5; 5); Resources requirements (5; 1);
Implementation simplicity (5; 3)
The solution proposed by the MPS system is presented in Figure 2. Using a similarity threshold of 0.5,
the retrieve step finds eight cases, grouped into three model categories. Explaining the figure (from
right to left), the proposed solution includes mining plans of the (three) DM functions and model
categories (columns IV and V). The evaluation criteria average values are presented on column III.
Each model category is instantiated with the most similar case, providing: on (II) its similitude to the
target; at the left side of (I), the (most similar) case’s number hyperlink, to access its detailed
information; at the right side of (I), the combo boxes to access further information about other (less
similar) cases of the model category; each combo box may be expanded to see the case’s number
and its similitude to the target, as well to access further information of the case.
I II
III IV V
Figure 2: A problem solving solution description excerpt
The proposed plans are all appropriate. Association rules is frequently used to discover relations
among learning activities and sequential analysis to extract interesting patterns in the sequences of
on-line activities, and clustering to group similar access behaviours (Zaiane 2001). Hierarchical
clustering model (case 6) was a similarity value substantially inferior and is a different king of
clustering. By default and as intended, the system gives emphasis to the similarity between datasets.
This is considered a good result, from the experimental work, since dataset characteristics are the
most important features of mining methods choice. The analyses from cases 8 and 9 were performed
using datasets most similar to the target. The selection of the goal “Determine access order of pages
and items” also favours the sequential model. The association rules model represents a good option,
given the better compromise between precision and coverage. Yet, the inclusion of the hierarchical
clustering plan may be useful, although the model is lesser suited to the problem than the association
rules, which is more informative and accurate (i.e. provides rules with support and confidence).
4.3 Exemplifying learning
WUM processes description encompasses data from several dispersed and heterogeneous sources.
Sources include databases or files, containing “unlimited” datasets, pre-processing and KD tools,
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