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Encrypted prefix tree for pattern mining

Bhattacharyya R., Bhattacharyya B., Chaudhuri A.

Foundations of Computing and Decision Sciences

2009

Vol. 34, No. 1

3-22

Data influx at large volumes is welcome for quality outcome in knowledge discovery, but it causes concern for scalability of mining algorithms. We introduce three measures for scalable mining - bit-vector coding, data-partitioning and Transaction Prefix (TP)-tree. Following encryption with bit-vector coding, transaction records are partitioned with notion of common prefixes. A TP-tree structure is devised for arranging the data parts such that multiple records share common storage. Advantage is two-fold: additional storage reduction over bit-vector coding and mining common prefixes together. These altogether improve space-time requirement in frequent pattern mining. Experiments on dense datasets show significant improvements in performance and scalability of both candidate generation and pattern-growth algorithms.

Automatic selection of structuring element for object classification through morphology

Chanda B., Bhattacharyya R.

Image Processing & Communications

1999

Vol. 5, no 1

67-79

How to select a structuring element for a given task is one of the most frequently asked questions in morphology. Present work tries to give a solution for a restricted class of problems, namely shape classification. In this work an algorithm that extracts distinctive structure of each of a given set of objects is proposed. The proposed algorithm is based on a new approach for computing distance transform.