Structure Discovery from Sequential Data
نویسندگان
چکیده
Abstract In this paper we describe I-Subdue, an extension to the Subdue graph-based data mining system. I-Subdue operates over sequentially received relational data to incrementally discover the most representative substructures. The ability to incrementally refine discoveries from serially acquired data is important for many applications, particularly as computer systems become more integrated into human lives as interactive assistants. This paper describes initial work to overcome the challenge of locally optimal substructures overshadowing those that are globally optimal. We conclude by providing an overview of additional challenges for sequential structure discovery.
منابع مشابه
Does Fundraising Have Meaningful Sequential Patterns? The Case of Fintech Startups
Nowadays, fundraising is one of the most important issues for both Fintech investors and startups. The pattern of fundraising in terms of “number and type of rounds and stages needed” are important. The diverse features and factors that could stem from Fintech business models which can influence success are of the key issues in shaping these patterns. This study applied the top 100 KPMG Fintech...
متن کاملA Single-scan Algorithm for Mining Sequential Patterns from Data Streams
Sequential pattern mining (SPAM) is one of the most interesting research issues of data mining. In this paper, a new research problem of mining data streams for sequential patterns is defined. A data stream is an unbound sequence of data elements arriving at a rapid rate. Based on the characteristics of data streams, the problem complexity of mining data streams for sequential patterns is more ...
متن کاملAb Initio Study of Vinblastine-Tubulin Anticancer Complex
Vinblastine is an important anticancer agent known to diminish microtubule assembly. Ab initio calculations are applied to examine the structural properties and different energies of vinblastine-tubulin complex in different dielectric constants and temperatures. The aims of this work are discovery the best optimized structure and thermodynamic properties of vinblastine-tubulin complex ...
متن کاملBalanced Constraint Measure Algorithm to Preserve Privacy from Sequential Rule Discovery
Preservation of needed privacy from mining algorithms (data mining methods which extract information from the privacy diffusion of people and organizations) is an emerging research area. Researchers are creating procedures to maintain a proper balance between maintaining information privacy and knowledge discovery by using data mining. In this paper, we initially use the prefixspan algorithm to...
متن کاملFuzzy multiple-level sequential patterns discovery from customer transaction database
Sequential pattern discovery is a very important research topic in data mining and knowledge discovery and has been widely applied in business analysis. Previous works were focused on mining sequential patterns at a single concept level based on definite and accurate concept which may not be concise and meaningful enough for human experts to easily obtain nontrivial knowledge from the rules dis...
متن کامل