7.3
Trabalhos Futuros
Existem v´arias maneiras de estender o trabalho apresentado nesta Disserta¸c˜ao. Uma delas ´e utilizar outras bases de dados reais. Al´em disso, o framework pode ser estendido para lidar com vari´aveis simb´olicas modais, em que ´e associado um peso, freq¨uˆencia ou probabilidade a cada categoria, indicando qu˜ao freq¨uente, t´ıpica ou relevante ´e a categoria considerada para o objeto em quest˜ao.
Uma avalia¸c˜ao adicional pode levar em considera¸c˜ao a complexidade computacional e o tempo de execu¸c˜ao de cada algoritmo de agrupamento considerado.
Ao framework proposto tamb´em pode ser adicionado um m´odulo de tratamento de dados faltosos.
Uma avalia¸c˜ao adicional pode considerar o tratamento dos intervalos atrav´es de um algoritmo de otimiza¸c˜ao. O algoritmo de Fisher (FISHER, 1958) pode ser utilizado para fazer a convers˜ao de intervalos em categorias de forma otimizada. O algoritmo de Fisher ´e um algoritmo de programa¸c˜ao dinˆamica eficiente que minimiza a variˆancia intra-classe (LECHEVALLIER, 1976).
O m´odulo de avalia¸c˜ao pode ser melhorado com a inclus˜ao de ´ındices de valida¸c˜ao espec´ıficos para algoritmos fuzzy, como por exemplo, os ´ındices apresentados por (CAM- PELLO, 2007).
A abordagem CARD (FRIGUI; HWANG; RHEE, 2007) mostrou-se bastante promissora nos experimentos realizados, principalmente se tratando de bases de dados reais. Essa abordagem pode ser utilizada para estender m´etodos de agrupamento diferentes dos apre- sentados neste trabalho. Al´em disso, podem ser propostas novas medidas de dissimilari- dade que n˜ao considerem igual relevˆancia dos atributos.
Finalmente, a metodologia apresentada pode ser utilizada com proveito para a ob- ten¸c˜ao de grupos homogˆeneos de perfis de usu´arios no contexto da aplica¸c˜ao da minera¸c˜ao de dados em arquivos log (Web usage Mining - WUM) (COOLEY; SRIVASTAVA; MOBASHER, 1997).
126
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