Zero-Inflated Patent Data Analysis Using Generating Synthetic Samples

Abstract

Due to the expansion of the internet, we encounter various types of big data such as web documents or sensing data. Compared to traditional small data such as experimental samples, big data provide more chances to find hidden and novel patterns with big data analysis using statistics and machine learning algorithms. However, as the use of big data increases, problems also occur. One of them is a zero-inflated problem in structured data preprocessed from big data. Most count values are zeros because a specific word is found in only some documents. In particular, since most of the patent data are in the form of a text document, they are more affected by the zero-inflated problem. To solve this problem, we propose a generation of synthetic samples using statistical inference and tree structure. Using patent document and simulation data, we verify the performance and validity of our proposed method. In this paper, we focus on patent keyword analysis as text big data analysis, and we encounter the zero-inflated problem just like other text data.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 7 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.