Abstract. This paper describes a data mining approach to the problem of detecting erroneous foreign trade transactions in data collected by the Portuguese Institute of Statistics (INE). Erroneous transactions are a minority, but still they have an important impact on the official statistics produced by INE. Detecting these rare errors is a manual, timeconsuming task, which is constrained by a limited amount of available resources (e.g. financial, human). These constraints are common to many other data analysis problems (e.g. fraud detection). Our previous work addresses this issue by producing a ranking of outlyingness that allows a better management of the available resources by allocating them to the most relevant cases. It is based on an adaptation of hierarchical clustering methods for outlier detection. However, the method cannot be applied to articles with a small number of transactions. In this paper, we complement the previous approach with some standard statistical methods for outlier detection for handling articles with few transactions. Our experiments clearly show its advantages in terms of the criteria outlined by INE for considering any method applicable to this business problem. The generality of the approach remains to be tested in other problems which share the same constraints (e.g. fraud detection).
We didn't start the fire, it was always burning since technology became integrated into wearable things that can be traced back to the early 1500s. This earliest forms of wearable technology were manifested as pocket watches. Of course technology changed and evolved, but again it might be the watch, now in form of a wrist worn smart watch, that could carve the way towards an always on, large scale, planet spanning, body sensor network. The challenge arises on how to handle this enormous scale of upcoming smart watches and the produced data. This work highlights a strategy on how to make use of the massive amount of smart watches in building goal oriented, dynamically evolving network structures that autonomously adapt to changes in the smart watch ecosystem like cells do in the human organism.
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