• Dr.P.YOGANANDHINI

Exploring event sequences in big data is challenging. Sequential pattern mining finds applications in numerous diverging fields. Due to the problem’s combinatorial nature, two main challenges arise. First, existing algorithms output large numbers of patterns many of which are uninteresting from a user’s perspective. Second, as datasets grow, mining large numbers of patterns gets computationally expensive. Though many mining algorithms have been developed to derive the most frequently occurring and the most meaningful sequential patterns, it is yet difficult to make sense of the results. This work tackles this problem by combining interactive visualization with sequential pattern mining in order to create a “transparent box” execution model. Our proposed approach describes the design of Peek quence, which aims to increase the interpretability of machine learning-based sequence mining algorithms.

sequential pattern mining, data mining, machine learning.

"EXPLORATORY VISUAL SEQUENCE MINING BASED ON PATTERN-GROWTH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.g520-g523, July-2023, Available :http://www.jetir.org/papers/JETIR2307673.pdf

"EXPLORATORY VISUAL SEQUENCE MINING BASED ON PATTERN-GROWTH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppg520-g523, July-2023, Available at : http://www.jetir.org/papers/JETIR2307673.pdf