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One of the crucial fields of study that is getting more attention is association rule mining. It is crucial to databases' knowledge discovery (KDD). KDD and association rule mining have a very broad use. It has evolved at a rapid rate over the past fifteen years. Association Rule Mining is a novel technology, although it is still in the discovery and development stages.
Video is an illustration of interactive media data since it contains text, pictures, meta-data, visual, sound, and different kinds of data. It is frequently used for a variety of important likely reasons, such as security and reconnaissance, amusement, healthcare, educational endeavours, and sports. One of the main issues for the local data-mining research community is video data mining, which aims to find and comprehend fascinating examples from the large volume of video data. Video data mining is still in its infancy when compared to mining other sorts of data.
It is pivotal to grasp and naturally mine the information from video databases considering the continually extending computerized libraries and video databases. Differentiating association rules between components in a significant video library is critical in the field of video data mining. As per continuous imaginative work, association rule mining is being utilized rapidly in various tasks, including perception, social affairs, broadcast news, sports, records, films, clinical data, as well as on the web and individual media assortments. This exploration tries to give a far reaching method to association rule extraction from video databases.

1. Introduction -- 2. Literary Evolutionism and Its Discontents: Between Zhou Zuoren and Lu Xun -- 3. Individuality contra "Homo Economicus": A Reading of Lao She's Camel-Xiangzi -- 4. Touch, Body, and the New Perceptionism: Mu Shiying's Case -- 5. Lu Xun's "Ah Jin" and the Politics of Exemplarity

AbstractWe compare trade size clustering of morning, afternoon, and after‐hours trades in both the E‐mini S&P 500 and E‐mini NASDAQ‐100 futures markets. Morning and afternoon volatility is higher than after‐hours volatility. Morning and afternoon trades cluster more at round sizes than do after‐hours trades, and morning and afternoon trades cluster more on days with macroeconomic announcements than without announcements. Taken together, our results are consistent with the prior literature that trade size clustering increases with volatility.