IND 是一个开源的系统，可以处理大部分的独立事件，而这些独立事件都是使用固定长度的向量描述的值。IND 提供了一系列的功能特征和使用风格，主要是为了方便普通用户以及高级用户或者是那些对调查研究感兴趣的人使用。IND 是由四个基础的例程组成：数据操作例程、目录生成例程、目录检测例程和目录显示例程。
IND is applicable to most data sets consisting of independent instances, each described by a fixed length vector of attribute values. An attribute value may be a number, one of a set of attribute specific symbols, or omitted. One of the attributes is delegated the "target" and IND grows trees to predict the target. Prediction can then be done on new data or the decision tree printed out for inspection.
IND provides a range of features and styles with convenience for the casual user as well as fine-tuning for the advanced user or those interested in research. IND can be operated in a Breiman/Friedman/ Olshen/Stone-like mode (but without regression trees, surrogate splits or multivariate splits), and in a mode like C4.5. Advanced features allow more extensive search, interactive control and display of tree growing, and Bayesian and MML algorithms for tree pruning and smoothing. These often produce more accurate class probability estimates at the leaves.
IND also comes with a comprehensive experimental control suite. IND consist of four basic kinds of routines; data manipulation routines, tree generation routines, tree testing routines, and tree display routines. The data manipulation routines are used to partition a single large data set into smaller training and test sets. The generation routines are used to build classifiers. The test routines are used to evaluate classifiers and to classify data using a classifier. And the display routines are used to display classifiers in various formats.
IND is written in K&R C, with controlling scripts in the "csh" shell of UNIX, and extensive UNIX man entries. It is designed to be used on any UNIX system, although it has only been thoroughly tested on SUN platforms. IND comes with a manual giving a guide to tree methods, and pointers to the literature, and several companion documents.