Clustering Mutual Funds Based on Investment Similarity

AbstractIt is risky to invest to single or similar mutual funds because the variance of the return becomes large. Mutual funds are categorized based on the investment strategy by a company that rated funds based on performance, but the fund categories are different from its actual operations. While some previous studies have proposed methods to cluster mutual funds based on the historical performances, we cannot apply these methods to new mutual funds. In this paper, we clusters mutual funds based on the investment similarity instead of the historical performances. The contributions of this paper are: 1. To propose two new methods for classifying mutual funds based on the investment similarity, 2. To evaluate the proposed methods based on actual 551 Japanese mutual funds

Comparison of Three Parallel Implementations of an Induction Algorithm

Recently, researchers have tried to apply ILP to KDD because ILP enlarges the applicability of Machine Learning to cover KDD and Data Mining: it enables them to learn from multiple relational tables. Many scientific discovery systems are motivated from the desire to deal with larger databases. However the larger the databases are, the more computational power we need. Parallel computing is a possible solution to this problem. This research also aims to implement QUINLAN 's Foil in parallel. Foil finds definitions of relations using other relations in background knowledge with a top-down approach. There are two approaches to designing parallel algorithms for inductive learning, the search space parallel approach and the data parallel approach. In ILP data sets consist of training sets and background knowledge. Thus we examine three approaches, to part the search space, the training set, and the background knowledge. We experimented on FUJITSU AP3000 to compare among these approaches. Ex..