Since the middle of the 20th century, various types of automatic music
have been attempted. Usually, these attempts utilized random number or
stochastic information to generate music.
In these trials, the researches focused on the automatic generation of
Indeed, their trials are important for art and science. However,
from the viewpoint of entertainment,
it is more important to generate music by ourselves with the proper
assistance or guidance from a computer.
We are now developing a symbolically rich interactive music composition
system, the Augmented Composer, that uses atomic music patterns
and controls their duration, pitch, and
velocity to generate music. Its interface is quite simple and easy, so even
a non-expert can compose ``music''.
Through analysis of
composed music (MIDI data), we found an explicit
difference between music by expert composers and novices composers.
Therefore, We propose a music composition support (suggestion) system from the viewpoint of active mining.
Our future goal includes a method to stimulate the users' creativity by concepts such as chance discovery.
If we analyze the data (perform a data mining) that is collected
during a medical care, it is slightly difficult to obtain proper
results. The reason for the difficulty is followings;
The data is not collected with an aiming to obtain a standard value set for a medical diagnosis but for a medical diagnosis of the special patients. In addition, since the type of medical inspection depends upon patient's health status, items and frequency of the medical inspections are different according to the patients. For example, to not so serious patients, usually, only medical inspections which an insurance can be applied will be performed, and a frequency of medical inspection is not so high. On the contrary, to serious patients, all the necessary medical inspections will be performed even if they are quite expensive, and a frequency of medical inspection is quite high. Thus, the data collected during medical care usually contains various types of data and deviation. In addition, since the amount of data that can be collected in one year is not so large, it will not be sufficient to perform data mining on the data set. Even if the amount of a data set is sufficient, a lot of data is missing and the data is heterogeneous.