Due to the availability of internet-based abstract services and patent databases, bibliometric analysis has become one of key technology forecasting approaches. Recently, latent semantic analysis (LSA) has been applied to improve the accuracy in document clustering. In this paper, a new LSA method, probabilistic latent semantic analysis (PLSA) which uses probabilistic methods and algebra to search latent space in the corpus is further applied in document clustering. The results show that PLSA is more accurate than LSA and the improved iteration method proposed by authors can simplify the computing process and improve the computing efficiencyDebido a la disponibilidad de servicios abstractos de internet y bases de datos de patentes, un análisis bibliométrico se ha transformado en una aproximación clave de sondeo de tecnologías. Recientemente, el Análisis Semántico Latente (LSA) ha sido aplicado para mejorar la precisión en el clustering de documentos. En el siguiente trabajo se muestra, un nuevo método LSA, el Análisis Semántico Probabilística Latente (PLSA), que utiliza métodos probabilísticas y álgebra para buscar espacio latente en el cuerpo generado por el clustering de documentos. Los resultados demuestran que PLSA es más preciso que LSA y mejora el método de iteración propuesto por autores que simplifican los procesos de computación y mejoran la eficiencia de cómputo.Due to the availability of internet-based abstract services and patent databases, bibliometric analysis has become one of key technology forecasting approaches. Recently, latent semantic analysis (LSA) has been applied to improve the accuracy in document clustering. In this paper, a new LSA method, probabilistic latent semantic analysis (PLSA) which uses probabilistic methods and algebra to search latent space in the corpus is further applied in document clustering. The results show that PLSA is more accurate than LSA and the improved iteration method proposed by authors can simplify the computing process and improve the computing efficiency