{'fraction_non_alphanumeric': 0.012236573759347382, 'fraction_numerical': 0.0, 'mean_word_length': 5.63063063063063, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Exploiting location based social networks in business predictions

The growing use of Location Based Social Networks especially in recent years provides large amount of data transactions. These data transactions attract many data mining researchers to infer various information from them. In this paper, a geographic business prediction technique is proposed, which infers business usage by exploiting data published about venues in Location Based Social Networks. The proposed technique is beneficial for investors and business decision makers. The proposed geo-business prediction technique considers spatial and categorical factors in the prediction process. Both factors affect the prediction accuracy rather than using traditional spatial prediction techniques, which are usually used where only the location feature is involved in the prediction process. Additionally, an outlier filter is proposed and applied to the data to avoid extreme values involvement in the prediction process in order to achieve better prediction accuracy. To test the proposed technique, an experimental case study is implemented. It uses data extracted from Foursquare about business venues in Texas State in the United States of America. The proposed geo business prediction technique has shown to provide better prediction accuracy than k nearest neighbor spatial prediction. The Application of the outlier filter, results in even higher prediction accuracy for the proposed technique.