Articles written in Sadhana
Volume 29 Issue 6 December 2004 pp 589-604
A couple of non-convex search strategies, based on the genetic algorithm, are suggested and numerically explored in the context of large-deflection analysis of planar, elastic beams. The first of these strategies is based on the stationarity of the energy functional in the equilibrium state and may therefore be considered weak. The second approach, on the other hand, attempts to directly solve the governing differential equation within an optimisation framework and such a solution may be thought of as strong. Several numerical illustrations and verifications with ‘exact’ solutions, if available, are provided
Volume 41 Issue 12 December 2016 pp 1369-1380
‘‘Sequential pattern mining’’ is a prominent and significant method to explore the knowledge and innovation from the large database. Common sequential pattern mining algorithms handle static databases.Pragmatically, looking into the functional and actual execution, the database grows exponentially thereby leading to the necessity and requirement of such innovation, research, and development culminating into the designing of mining algorithm. Once the database is updated, the previous mining result will be incorrect, and we need to restart and trigger the entire mining process for the new updated sequential database. To overcome and avoid the process of rescanning of the entire database, this unique system of incremental mining of sequential pattern is available. The previous approaches, system, and techniques are a priori-based frameworks but mine patterns is an advanced and sophisticated technique giving the desired solution. We propose and incorporate an algorithm called STISPM for incremental mining of sequential patterns using the sequence treespace structure. STISPM uses the depth-first approach along with backward tracking and the dynamic lookahead pruning strategy that removes infrequent and irregular patterns. The process and approach from the root node to any leaf node depict a sequential pattern in the database. The structural characteristic of the sequence tree makes it convenient and appropriate for incremental sequential pattern mining. The sequence tree also stores all the sequential patterns with its count and statistics, so whenever the support system is withdrawn or changed, our algorithm using frequent sequence tree as the storage structure can find and detect all the sequential patternswithout mining the database once again.