Anomaly£º¼ûÒì³£Öµ´ÊÌõ¡£
Apache Software Foundation£¨ASF£©£º×¨ÃÅΪ֧³Ö¿ªÔ´Èí¼þÏîÄ¿¶ø°ìµÄÒ»¸ö·ÇÓ¯ÀûÐÔ×éÖ¯¡£
ARPU£¨Average revenue per user£©£ºÃ¿¸öÓû§µÄƽ¾ùÊÕÈë¡£
Artificial neural network:È˹¤Éñ¾ÍøÂ磬ͨ³£¼ò³ÆÉñ¾ÍøÂç¡£
Avro£ºÒ»¸öÔÚHadoopÉϵÄÊý¾ÝÐòÁл¯ÏµÍ³£¬Éè¼ÆÓÃÓÚÖ§³Ö´óÅúÁ¿Êý¾Ý½»»»Ó¦Óá£
±´Ò¶Ë¹·ÖÎö·½·¨£¨Bayesian Analysis£©£ºÌṩÁËÒ»ÖÖ¼ÆËã¼ÙÉè¸ÅÂʵķ½·¨£¬ÕâÖÖ·½·¨ÊÇ»ùÓÚ¼ÙÉèµÄÏÈÑé¸ÅÂÊ¡¢¸ø¶¨¼ÙÉèϹ۲쵽²»Í¬Êý¾ÝµÄ¸ÅÂÊÒÔ¼°¹Û²ìµ½µÄÊý¾Ý±¾Éí¶øµÃ³öµÄ¡£
bounce rate£º¼ûÌø³öÂÊ´ÊÌõ¡£
B2C£ºÓ¢ÎÄBusiness-to-ConsumerµÄËõд£¬ÆäÖÐÎĺ¬ÒåΪÆóÒµ¶ÔÏû·ÑÕß¡£
CART£ºClassification and Regression TreesµÄÓ¢ÎÄÊ××ÖĸËõд£¬»òÕ߳ƷÖÀàÓë»Ø¹éÊ÷£¬ÊÇÒ»ÖÖ¾ö²ßÊ÷·ÖÀàËã·¨¡£
CBL£¨China Black List£©£ºÖйúÀ¬»øÓʼþºÚÃûµ¥¡£
Cluster£¨Àà»ò´ØµÄÓ¢ÎÄ£©£ºÊÇÒ»¸öÊý¾Ý¶ÔÏóµÄ¼¯ºÏ¡£
Cookie: Ö¸µÄÊÇÖ¸ÍøվΪÁ˱æ±ðÓû§Éí·Ý¶ø´¢´æÔÚÓû§±¾µØÖÕ¶Ëä¯ÀÀÆ÷ÉϵÄÒ»ÀàÊý¾Ý¡£
CRM£¨Óû§¹Øϵ¹ÜÀí£¬Customer Relationship Management£©Ö¸µÄÊǹ«Ë¾¶Ô¿Í»§ºÍDZÔÚ¿Í»§µÄ¹ÜÀíģʽ¡£
Direct Marketing£º¼ûֱЧÐÐÏú´ÊÌõ¡£ Discriminant analysis£º¼ûÅбð·ÖÎö´ÊÌõ¡£
DSS(Decision Support System)£º¾ö²ßÖ§³ÖϵͳµÄËõд£¬ÊǸ¨Öú¾ö²ßÕßͨ¹ýÊý¾Ý¡¢Ä£ÐͺÍ֪ʶ£¬½øÐаë½á¹¹»¯»ò·Ç½á¹¹»¯¾ö²ßµÄ¼ÆËã»úÓ¦ÓÃϵͳ¡£
¶ÀÁ¢·Ã¿Í£ºÖ¸ÔÚÒ»ÌìÖ®ÄÚ£¨00:00-24:00£©·ÃÎÊÍøÕ¾µÄÉÏÍøµçÄÔÊýÁ¿£¨ÒÔcookieΪÒÀ¾Ý£©¡£
EB£º¼ÆËã»ú´æ´¢µ¥Î»£¬1 EB = 1,024 PB = 1,048,576 TB = 1,152,921,504,606,846,976 Bytes£¨×Ö½Ú£©£¬»òÊÇ2µÄ60´Î·½×Ö½Ú¡£
EDM£¨Email Direct Marketing£©£ºÓõç×ÓÓʼþ½øÐÐÓªÏúµÄ·½Ê½¡£
EIS£¨Executive Information SystemsµÄËõд£¬¸ß¼¶¹ÜÀíÈËÔ±ÐÅϢϵͳ)£ºÎª¸ß¼¶¹ÜÀíÈËÔ±Éè¼ÆµÄϵͳ£¬ÓÃÓÚÉî²ã´Î¹ÜÀíÊý¾Ý·ÖÎöºÍÔËÓªÇ÷ÊÆ·ÖÎöµÈ¡£
Entropy£º¼ûìØ¡£
¶þÌøÂÊ£ºµ±ÍøÕ¾Ò³ÃæÕ¹¿ªºó£¬Óû§ÔÚÒ³ÃæÉϲúÉúµÄÊ״εã»÷±»³ÆΪ¡°¶þÌø¡±£¬¶þÌøµÄ´ÎÊý¼´Îª¡°¶þÌøÁ¿¡±£¬¶ø¶þÌøÁ¿Óëä¯ÀÀÁ¿µÄ±ÈÖµ³ÆΪҳÃæµÄ¶þÌøÂÊ¡£
ETL£º(Extract Transform Load)µÄËõд£¬ÊÇÖ¸Êý¾ÝµÄÌáÈ¡¡¢×ª»»¡¢¼ÓÔØ¡£
·Ö²¼Ê½Êý¾Ý¿â£¨Distributed Database£©£ºÓüÆËã»úÍøÂ罫ÎïÀíÉÏ·ÖÉ¢µÄ¶à¸öÊý¾Ý¿âµ¥ÔªÁ¬½ÓÆðÀ´×é³ÉÒ»¸öÂ߼ͳһµÄÊý¾Ý¿â¡£
¹ØÁª¹æÔò(Association
rules)£ºÊÇÐÎÈçX¡úYµÄÔ̺ʽ,ÆäÖÐXºÍY·Ö±ð³ÆΪ¹ØÁª¹æÔòµÄÏȵ¼(antecedent»òleft-hand-side,
LHS)ºÍºó¼Ì(consequent»òright-hand-side, RHS) ¡£
¸ù½Úµã£º¾ö²ßÊ÷×îÉÏÃæµÄ½Úµã¡£ÔÚËüÉÏÃæûÓÐÆäËû½Úµã£¬ÆäËûËùÓеÄÊôÐÔ¶¼ÊÇËüµÄºóÐø½Úµã¡£
¹ºÎïÀº·ÖÎö£¨market basket analysis£©£º¾ÍÊǹØÁª¹æÔòËã·¨¡£ÔÚÊг¡ÉϹØÁª¹æÔòËã·¨¾³£×÷ΪÉÌÆ·¹ºÎï³µµÄ·ÖÎö£¬ËùÒÔÔÚÓ¦ÓÃÁìÓòÓÖ±»³ÆΪ¹ºÎïÀº·ÖÎö¡£
Granularity£º¼û¡°Á£¶È¡±¡£
HBase £ºÒ»¸öÔÚHDFSÉϴ´ó¹æÄ£½á¹¹»¯´æ´¢¼¯Èº·Ö²¼Ê½´æ´¢ÏµÍ³£¬¾ßÓи߿ɿ¿ÐÔ¡¢¸ßÐÔÄÜ¡¢ÃæÏòÁУ¬¿ÉÉìËõÌØÐÔ¡£
HDFS£º²¿ÊðÔÚÁ®¼ÛÓ²¼þÉÏÌṩ¸ßÍÌÍÂÁ¿ºÍ¸ßÈÝ´íÐԵķֲ¼Ê½Îļþϵͳ£¬ÊʺÏÓг¬´óÊý¾Ý¼¯µÄÓ¦ÓóÌÐò¡£
Hive£º»ùÓÚHadoopµÄÊý¾Ý²Ö¿â¹¤¾ß£¬¿ÉÒÔ½«½á¹¹»¯µÄÊý¾ÝÓ³Éä³ÉÊý¾Ý±í²¢ÌṩÀàSQLÊý¾Ý¿â²éѯ¹ÜÀí¹¦ÄÜ£¬ÊʺÏÓÚÊý¾Ý²Ö¿âµÄͳ¼Æ·ÖÎö¡£
ºóÑé¸ÅÂÊ(Posterior Probability)£ºµ±¸ù¾Ý¾Ñé¼°ÓйزÄÁÏÍƲâ³öÖ÷¹Û¸ÅÂʺ󣬶ÔÆäÊÇ·ñ׼ȷûÓгä·Ö°ÑÎÕʱ£¬¿É²ÉÓøÅÂÊÂÛÖеı´Ò¶Ë¹¹«Ê½½øÐÐÐÞÕý£¬ÐÞÕýÇ°µÄ¸ÅÂʳÆΪÏÈÑé¸ÅÂÊ£¬ ÐÞÕýºóµÄ¸ÅÂʳÆΪºóÑé¸ÅÂÊ¡£
»Ø¹é·ÖÎö£¨regression analysis)ÊÇÈ·¶¨Á½ÖÖ»òÁ½ÖÖÒÔÉϱäÊý¼äÏ໥ÒÀÀµµÄ¶¨Á¿¹ØϵµÄÒ»ÖÖͳ¼Æ·ÖÎö·½·¨¡£
¼ÆÁ¿¾¼Ãѧ£¨Econometrics£©ÊÇÒÔ¾¼ÃѧºÍÊýÀíͳ¼ÆѧΪ·½·¨ÂÛ×÷Ϊ»ù´¡£¬¶ÔÓÚ¾¼ÃÎÊÌâÊÔͼÓÃÊýÁ¿ºÍ¾ÑéÁ½Õß½øÐÐ×ۺϵľ¼Ãѧ·ÖÖ§¡£
»ùÓÚ»¥ÁªÍøµÄÍÚ¾ò£¨WebÍÚ¾ò£©ÊÇÀûÓÃÊý¾ÝÍÚ¾ò¼¼Êõ´ÓWebÎĵµ¼°Web·þÎñÖÐ×Ô¶¯·¢ÏÖ²¢ÌáÈ¡ÈËÃǸÐÐËȤµÄÐÅÏ¢¡£
½»²æÑéÖ¤(Cross-validation)£ºÖ÷ÒªÓÃÓÚ½¨Ä£Ó¦ÓÃÖУ¬ÔÚ¸ø¶¨µÄ½¨Ä£Ñù±¾ÖУ¬Äóö´ó²¿·ÖÑù±¾½øÐн¨Ä£ÐÍ£¬ÁôС²¿·ÖÑù±¾Óøս¨Á¢µÄÄ£ÐͽøÐÐÔ¤±¨£¬²¢ÇóÕâС²¿·ÖÑù±¾µÄÔ¤±¨Îó²î£¬¼Ç¼ËüÃǵÄƽ·½¼ÓºÍ¡£
»úÆ÷ѧϰ(Machine Learning)£ºÑо¿¼ÆËã»úÔõÑùÄ£Äâ»òʵÏÖÈËÀàµÄѧϰÐÐΪ£¬ÒÔ»ñȡеÄ֪ʶ»ò¼¼ÄÜ£¬ÖØÐÂ×éÖ¯ÒÑÓеÄ֪ʶ½á¹¹Ê¹Ö®²»¶Ï¸ÄÉÆ×ÔÉíµÄÐÔÄÜ¡£
¼à¶½Ê½Ñ§Ï°£¨Supervised
learning£©£º»úÆ÷ѧϰÖеÄÒ»À࣬¿ÉÒÔÓÉѵÁ·×ÊÁÏÖÐѧµ½»ò½¨Á¢Ò»¸öģʽ£¨º¯Êý£©£¬²¢ÒÀ´ËģʽÍƲâеÄÑù±¾¹éÀà»òÕßÊôÐÔ¡£
¾ÛÀà(Clustering)£º½«ÎïÀí»ò³éÏó¶ÔÏóµÄ¼¯ºÏ·Ö³ÉÓÉÀàËƵĶÔÏó×é³ÉµÄ¶à¸öÀàµÄ¹ý³Ì¡£ÓɾÛÀàËùÉú³ÉµÄ´ØÊÇÒ»×éÊý¾Ý¶ÔÏóµÄ¼¯ºÏ£¬ÕâЩ¶ÔÏóÓëͬһ¸ö´ØÖеĶÔÏó±Ë´ËÏàËÆ£¬ÓëÆäËû´ØÖеĶÔÏóÏàÒì¡£
¾ö²ßÊ÷£¨Decision Tree£©£ºÒ»°ã¶¼ÊÇ×ÔÉ϶øϵÄÀ´Éú³ÉµÄ¡£Ã¿¸ö¾ö²ß»òʼþ£¨¼´×ÔȻ״̬£©¶¼¿ÉÄÜÒý³öÁ½¸ö»ò¶à¸öʼþ£¬µ¼Ö²»Í¬µÄ½á¹û£¬°ÑÕâÖÖ¾ö²ß·ÖÖ§»³ÉͼÐκÜÏñÒ»¿ÃÊ÷µÄÖ¦¸É£¬ ¹Ê³Æ¾ö²ßÊ÷¡£
¾ö²ßÊ÷¼ôÖ¦£¨Decision tree
pruning£©£ºÓÉÓÚÔÚ¾ö²ßÊ÷Éú³É¹ý³ÌÖУ¬»á¹ý¶ÈÄâºÏѵÁ·Êý¾Ý£¬¶øÇÒÒ×ÊÜÔëÉùÊý¾ÝµÄÓ°Ï죬ËùÒÔ¼ôÖ¦²Ù×÷ÊǾö²ßÊ÷Éú³É¹ý³ÌÖеÄÒ»¸öÖØÒª²½Öè¡£
¾ö²ßÖ§³Öϵͳ(decision support
system)£º¸¨Öú¾ö²ßÕßͨ¹ýÊý¾Ý¡¢Ä£ÐͺÍ֪ʶ£¬ÒÔÈË»ú½»»¥·½Ê½½øÐаë½á¹¹»¯»ò·Ç½á¹¹»¯¾ö²ßµÄ¼ÆËã»úÓ¦ÓÃϵͳ¡£
KDD(Knowledge discovery in database)£º·ºÖ¸ËùÓдÓÔ´Êý¾ÝÖз¢¾òģʽ»òÁªÏµµÄ·½·¨
k½üÁÚ(k nearest):Ò»¸öÀíÂÛÉϱȽϳÉÊìµÄ·½·¨£¬Ò²ÊÇ×î¼òµ¥µÄ»úÆ÷ѧϰËã·¨Ö®Ò»¡£¸Ã·½·¨µÄ˼·ÊÇ£ºÈç¹ûÒ»¸öÑù±¾ÔÚÌØÕ÷¿Õ¼äÖеÄk¸ö×îÏàËÆ(¼´ÌØÕ÷¿Õ¼äÖÐ×îÁÚ½ü)µÄÑù±¾ÖеĴó ¶àÊýÊôÓÚijһ¸öÀà±ð£¬Ôò¸ÃÑù±¾Ò²ÊôÓÚÕâ¸öÀà±ð¡£
LAMP£ºLinux£¬Apache£¬MySQLºÍPHP£¬ËÄÖÖweb¼¼ÊõµÄËõд£¬ÊÇһЩweb2.0¹«Ë¾Ê¹ÓõÄÖ÷Òª¼¼Êõ×éºÏ¡£
landing page£º¼û׎ҳ´ÊÌõ¡£
LBS£¨Location-based service£©ÊÇÓëλÖÃÏà¹ØµÄÈí¼þ·þÎñµÄÓ¢ÎÄËõд£¬Ö¸µÄÊÇÒ»ÀàÀûÓúͿØÖÆÓëλÖü°Ê±¼äÏà¹ØµÄ¼ÆËã»úÈí¼þ·þÎñ¡£ Á£¶È£¨Granularity£©£ºÖ¸Êý¾Ý²Ö¿âµÄÊý¾Ýµ¥Î»Öб£´æÊý¾ÝµÄϸ»¯»ò×ۺϳ̶ȵļ¶±ð¡£
Lift£ºÊ¹Ó÷ÖÀàÆ÷Ïà¶ÔÓÚ²»Ê¹Ó÷ÖÀàÆ÷²úÉúµÄÕýÀàµÄ±ÈÀý¡£ Áª»úÊÂÎñ´¦Àíϵͳ(OLTP)£ºÊµÊ±²É¼¯´¦ÀíÓëÊÂÎñÏàÁ¬µÄÊý¾ÝÒÔ¼°¹²ÏíÊý¾Ý¿âºÍÆäËüÎļþµÄµØλµÄ±ä»¯¡£ÔÚÁª»úÊÂÎñ´¦ÀíÖУ¬ÊÂÎñÊDZ»Á¢¼´Ö´Ðеģ¬ÕâÓëÅú´¦ÀíÏà·´£¬Ò»ÅúÊÂÎñ±»´æ´¢Ò»¶Îʱ¼ä£¬È»ºóÔÙ±»Ö´ÐС£
Áª»ú·ÖÎö´¦Àí(OLAP)£ºÊ¹·ÖÎöÈËÔ±£¬¹ÜÀíÈËÔ±»òÖ´ÐÐÈËÔ±Äܹ»´Ó¶à½Ç¶È¶ÔÐÅÏ¢½øÐпìËÙÒ»Ö£¬½»»¥µØ´æÈ¡£¬´Ó¶ø»ñµÃ¶ÔÊý¾ÝµÄ¸üÉîÈëÁ˽âµÄÒ»ÀàÈí¼þ¼¼Êõ¡£
Á÷Á¿£¨traffic£©£ºÊÇÖ¸ÍøÕ¾µÄ·ÃÎÊÁ¿£¬ÊÇÓÃÀ´ÃèÊö·ÃÎÊÒ»¸öÍøÕ¾»òÊÇÍøµêµÄÓû§ÊýÁ¿ÒÔ¼°Óû§Ëùä¯ÀÀµÄÍøÒ³ÊýÁ¿µÈһϵÁÐÖ¸±ê£¬ÕâЩָ±êÖ÷Òª°üÀ¨£º¶ÀÁ¢·Ã¿ÍÊýÁ¿£¨unique
visitors£©¡¢ ¡¤Ò³Ãæä¯ÀÀÊý£¨page views£©¡¢Ã¿¸ö·Ã¿ÍµÄÒ³Ãæä¯ÀÀÊý£¨Page Views per user£©¡£
Áù¶È·Ö¸ôÀíÂÛ£¨Six Degrees of
Separation£©£ºÊǸö¼ÙÉ裬ÔÚÈ˼ʹØϵÂöÂç·½ÃæÄú¿ÉÒÔͨ¹ý²»³¬³öÁùλÖмäÈËÖ±½ÓÓëÊÀÉÏÈÎÒâÈËÈÏʶ¡£
LNMP£ºLinux£¬Nginx£¬MySQLºÍPHP£¬ËÄÖÖweb¼¼ÊõµÄËõд£¬ÊÇһЩweb2.0¹«Ë¾Ê¹ÓõÄÖ÷Òª¼¼Êõ×éºÏ¡£
Metadata£º¼ûÔªÊý¾Ý¡£
MapReduce£ºHDFSÉÏ´¦Àí´óÊý¾Ý¼¯µÄ²¢ÐмÆËã¿ò¼Ü¡£
MongoDB: ÊÇÒ»¸ö»ùÓÚ·Ö²¼Ê½Îļþ´æ´¢µÄÊý¾Ý¿â¡£
Nginx£º¿ªÔ´µÄ¸ßÐÔÄÜHTTP·þÎñÆ÷¡£
Outlier: ¼ûÒì³£µã´ÊÌõ¡£
PAM£º¼ûΧÈÆÖÐÐĵãµÄ»®·Ö¾ÛÀàËã·¨¡£
Åбð·ÖÎö(Discriminant analysis)£ºÊÇÔÚ·ÖÀàÈ·¶¨µÄÌõ¼þÏ£¬¸ù¾ÝijһÑо¿¶ÔÏóµÄ¸÷ÖÖÌØÕ÷ÖµÅбðÆäÀàÐ͹éÊôÎÊÌâµÄÒ»ÖÖ¶à±äÁ¿Í³¼Æ·ÖÎö·½·¨¡£
PB£º¼ÆËã»ú´æ´¢µ¥Î»£¬1 PB = 1,024 TB = 1,048,576 GB = 1£¬125£¬899£¬906£¬842£¬624 Bytes£¨×Ö½Ú£©£¬»òÊÇ2µÄ50´Î·½×Ö½Ú¡£
PUѧϰ£ºÕýÀýºÍÎÞ±ê¼ÇÑù±¾Ñ§Ï°£¨Learning from Positive and Unlabeled
examples£©Ò»°ã³ÆΪLPU»òPUѧϰ£¬ÊÇÒ»ÖÖ°ë¼à¶½Ñ§Ï°·½·¨¡£
Pig£ºÔÚHDFSºÍMapReduceÉÏ´¦Àí´ó¹æÄ£Êý¾Ý¼¯µÄ½Å±¾ÓïÑÔ£¬ËüÌṩ¸ü¸ß²ã´ÎµÄ³éÏó²¢×ª»¯ÎªÓÅ»¯´¦ÀíµÄMapReduceÔËËã¡£
Ƶ·±¼¯£¨frequent itemset£©£ºÊÇ´óÓÚ×îС֧³Ö¶ÈµÄÏîÄ¿¼¯¡£
Ç¿¹ØÁª¹æÔò£ºÈç¹ûijÌõ¹æÔòͬʱÂú×ã×îС֧³Ö¶È£¨min-support£©ºÍ×îСÖÃÐŶȣ¨min-confidence£©£¬Ôò³ÆËüΪǿ¹ØÁª¹æÔò¡£ RÓïÑÔ£ºRÊÇÊôÓÚGNUϵͳµÄÒ»¸ö×ÔÓÉ¡¢Ãâ·Ñ¡¢Ô´´úÂ뿪·ÅµÄÈí¼þ£¬ÊÇÒ»¸öÓÃÓÚͳ¼Æ¼ÆËãºÍͳ¼ÆÖÆͼµÄ¹¤¾ß¡£
REST£¨Representational State Transfer£¬±íÏÖ״̬תÒÆ£©£ºÊÇRoy Fielding²©Ê¿ÔÚ2000ÄêËûµÄ²©Ê¿ÂÛÎÄÖÐÌá³öÀ´µÄÒ»ÖÖÈí¼þ¼Ü¹¹·ç¸ñ£¬ÔÚ´Ë·ç¸ñÖУ¬Ã¿¸ö×ÊÔ´ÊÇÓÉÈ«ÇòΨһµÄURIÀ´Ö¸¶¨£¬ ×ÊÔ´±¾ÉíºÍÆä±íÏÖ·½Ê½ÊÇÍêÈ«¶ÀÁ¢µÄ£»µ±Ò»¸öÓû§Äõ½×ÊÔ´µÄ±íÏÖ·½Ê½Ê±£¬ËûÓÐ×ã¹»µÄÐÅÏ¢¿ÉÒÔÐ޸ĻòÕßɾ³ý·þÎñÆ÷ÉÏÏàÓ¦µÄ×ÊÔ´¶øÇÒÿÌõÏûÏ¢¶¼°üº¬ÁË×ã¹»µÄÐÅÏ¢¿ÉÒÔÃèÊöÏûÏ¢µÄ´¦Àí¡£
ÈÈͼ£¨heat map£©£ºÈÈͼ»òÈÈÁ¦Í¼ÊÇÊý¾ÝµÄÒ»ÖÖ¶þά³ÊÏÖ£¬ÆäÖеÄÊýÖµ¶¼ÓÃÑÕÉ«±íʾ¡£Ò»¸ö¼òµ¥µÄÈÈͼÌṩÐÅÏ¢µÄ¼´Ê±¿É¼û¸Å¿ö¡£
È˹¤Éñ¾ÍøÂ磨Artificial Neural Networks£©£ºÒ»ÖÖÄ£·¶¶¯ÎïÉñ¾ÍøÂçÐÐΪÌØÕ÷£¬½øÐзֲ¼Ê½²¢ÐÐÐÅÏ¢´¦ÀíµÄËã·¨ÊýѧģÐÍ¡£ÕâÖÖÍøÂçÒÀ¿¿ÏµÍ³µÄ¸´Ôӳ̶ȣ¬Í¨¹ýµ÷ÕûÄÚ²¿ ´óÁ¿½ÚµãÖ®¼äÏ໥Á¬½ÓµÄ¹Øϵ£¬´Ó¶ø´ïµ½´¦ÀíÐÅÏ¢µÄÄ¿µÄ¡£
È˹¤ÖÇÄÜ(Artificial Intelligence)£ºÑо¿¡¢¿ª·¢ÓÃÓÚÄ£Äâ¡¢ÑÓÉìºÍÀ©Õ¹È˵ÄÖÇÄܵÄÀíÂÛ¡¢·½·¨¡¢¼¼Êõ¼°Ó¦ÓÃϵͳµÄÒ»ÃÅеļ¼Êõ¿Æѧ¡£ËüÆóͼÁ˽âÖÇÄܵÄʵÖÊ£¬ ²¢Éú²ú³öÒ»ÖÖеÄÄÜÒÔÈËÀàÖÇÄÜÏàËƵķ½Ê½×ö³ö·´Ó¦µÄÖÇÄÜ»úÆ÷¡£
3C²úÆ·£º3C²úÆ·Ö¸µÄÊÇͨѶ²úÆ·£¨Communication£©£¬Ïû·ÑÀàµç×Ó²úÆ·£¨Consumer
Electronics£©ºÍµçÄÔ²úÆ·£¨Computer£©£¬ÈýÀà²úÆ·µÄÊ××Öĸ¶¼ÊÇC£¬ËùÒÔ³Æ3C SEMMAÊÇÊý¾ÝÍÚ¾ò¹ý³Ì(Sample,
Explore, Modify, Model,and Assess)µÄÓ¢ÎÄËõд£¬Òâ˼ÊdzéÑù£¬¼ì²é£¬Ð޸ģ¬ÉèÁ¢Ä£ÐͺÍÆÀ¹À¡£
ìØ£¨entropy£©:Ö¸µÄÊÇÌåϵµÄ»ìÂҵij̶ȣ¬ËüÔÚ¿ØÖÆÂÛ¡¢¸ÅÂÊÂÛ¡¢ÊýÂÛ¡¢ÌìÌåÎïÀí¡¢ÉúÃü¿ÆѧµÈÁìÓò¶¼ÓÐÖØÒªÓ¦Óã¬ÔÚ²»Í¬µÄѧ¿ÆÖÐÒ²ÓÐÒýÉê³öµÄ¸üΪ¾ßÌåµÄ¶¨Ò壬ÊǸ÷ÁìÓòÊ®·ÖÖØÒªµÄ²ÎÁ¿¡£ìØÓɳµÀ·ò¡¤¿ËÀÍÐÞ˹£¨Rudolf
Clausius£©Ìá³ö£¬²¢Ó¦ÓÃÔÚÈÈÁ¦Ñ§ÖС£ºóÀ´ÔÚ£¬¿ËÀ͵¡¤°¬¶ûÎéµÂ¡¤ÏãÅ©£¨Claude Elwood
Shannon£©µÚÒ»´Î½«ìصĸÅÄîÒýÈëµ½ÐÅÏ¢ÂÛÖÐÀ´¡£ ÉÌÒµÖÇÄÜ£¨Business
Intelligence£©£º²ÉÓÃÊý¾Ý¿â»òÊý¾Ý²Ö¿â¼¼Êõ½øÐÐÉÌÒµÐÅÏ¢µÄÊÕ¼¯£¬¼¯³É£¬·ÖÎöºÍ±¨¸æÒÔ°ïÖú×ö¾ö²ßµÄÓ¦ÓÃÓëʵ¼ùϵͳ¡£
ʱ¼äÐòÁУ¨Time Series£©£ºÊÇÖ¸½«Ä³ÖÖÏÖÏóijһ¸öͳ¼ÆÖ¸±êÔÚ²»Í¬Ê±¼äÉϵĸ÷¸öÊýÖµ£¬°´Ê±¼äÏȺó˳ÐòÅÅÁжøÐγɵÄÐòÁС£Ê±¼äÐòÁз¨ÊÇÒ»ÖÖ¶¨Á¿Ô¤²â·½·¨£¬Òà³Æ¼òµ¥ÍâÑÓ·½·¨¡£
ÊÂÎñÊý¾Ý¿â(Transaction Database)£ºÓÉÎļþ¹¹³É£¬Ã¿Ìõ¼Ç¼´ú±íÒ»¸öÊÂÎñ¡£µäÐ͵ÄÊÂÎñ°üº¬Î¨Ò»µÄÊÂÎñ±ê¼Ç£¬¶à¸öÏîÄ¿×é³ÉÒ»¸öÊÂÎñ ¡£
Êý¾Ý½á¹¹£¨data structure)£º¸÷ÖÖÊý¾ÝÖ®¼äµÄÂß¼¹Øϵ£¬ÓÃÀ´Ö§³ÖÌض¨µÄÊý¾Ý´¦Àí¹¦ÄÜ£¬±ÈÈçÊ÷¡¢ÁбíºÍÁ´½Ó±í¡£
Êý¾Ý¿ÉÊÓ»¯(Data
Visualization)£º¹ØÓÚÊý¾ÝµÄÊÓ¾õ±íÏÖÐÎʽµÄÑо¿£¬ÕâÖÖÊý¾ÝµÄÊÓ¾õ±íÏÖÐÎʽ±»¶¨ÒåΪһÖÖÒÔijÖÖ¸ÅÒªÐÎʽ³éÌá³öÀ´µÄÐÅÏ¢£¬°üÀ¨ÏàÓ¦ÐÅÏ¢µ¥Î»µÄ¸÷ÖÖÊôÐԺͱäÁ¿¡£
Êý¾ÝÍÚ¾ò(Data
Mining)£º´Ó´æ·ÅÔÚÊý¾Ý¿â£¬Êý¾Ý²Ö¿â»òÆäËûÐÅÏ¢¿âÖеĴóÁ¿µÄÊý¾ÝÖлñÈ¡ÓÐЧµÄ¡¢ÐÂÓ±µÄ¡¢Ç±ÔÚÓÐÓõġ¢×îÖÕ¿ÉÀí½âµÄģʽµÄ¹ý³Ì¡£
Êý¾Ý¿ÉÊÓ»¯£¨Data Visualization£©£º¶àά¶ÈÊý¾Ýͨ¹ýͼÐεķ½Ê½À´×öµÄÕ¹ÏÖ
Êý¾Ý²Ö¿â£ºÊǾö²ßÖ§³Öϵͳ£¨DSS£©ºÍÁª»ú·ÖÎöÓ¦ÓÃÊý¾ÝÔ´µÄ½á¹¹»¯Êý¾Ý»·¾³¡£Êý¾Ý²Ö¿âÑо¿ºÍ½â¾ö´ÓÊý¾Ý¿âÖлñÈ¡ÐÅÏ¢µÄÎÊÌâ¡£Êý¾Ý²Ö¿âµÄÌØÕ÷ÔÚÓÚÃæÏòÖ÷Ìâ¡¢¼¯³ÉÐÔ¡¢Îȶ¨ÐÔºÍʱ±äÐÔ¡£
Êý¾ÝÇåÏ´(data
cleaning)£º¹ýÂËÄÇЩ²»·ûºÏÒªÇóµÄÊý¾Ý£¬½«¹ýÂ˵Ľá¹û½»¸øÒµÎñÖ÷¹Ü²¿ÃÅ£¬È·ÈÏÊÇ·ñ¹ýÂ˵ô»¹ÊÇÓÉÒµÎñµ¥Î»ÐÞÕýÖ®ºóÔÙ½øÐгéÈ¡¡£
Êý¾Ý¿â£¨Database£©£ºÊÇ°´ÕÕÊý¾Ý½á¹¹À´×éÖ¯¡¢´æ´¢ºÍ¹ÜÀíÊý¾ÝµÄ²Ö¿â¡£
ÊôÐÔ(attribute)£ºÊôÐÔÊÇʵÌåµÄÃèÊöÐÔÐÔÖÊ»òÌØÕ÷£¬¾ßÓÐÊý¾ÝÀàÐÍ¡¢Óò¡¢Ä¬ÈÏÖµÈýÖÖÐÔÖÊ¡£ÊôÐÔÒ²ÍùÍùÓÃÓڶԿؼþÌØÐÔµÄÃèÊö¡£¶ÔÓÚ°´Å¥¿Ø¼þµÄÃû³Æ¡¢ÏÔʾµÄÎÄ×Ö¡¢±³¾°É«£¬±³¾°Í¼Æ¬µÈµÈ¡£SNS£ºÊÇÉç»á»¯·þÎñÍøÂ磬Social
Services NetworksµÄÓ¢ÎÄÊ××ÖĸËõд¡£
spatio-temporal data mining£ºÊ±¼äºÍ¿Õ¼äÊý¾ÝµÄÍÚ¾ò¡£
Sqoop£ºÒ»¸öÓÃÀ´½«HadoopºÍ¹ØϵÐÍÊý¾Ý¿âÖеÄÊý¾ÝÏ໥תÒƵŤ¾ß¡£
Ë÷Òý£¨Index£©£ºÔÚÊý¾Ý¿âÖУ¬ÓÃÀ´¶Ô¼Ç¼ÌṩÓÐЧ·ÃÎʵıê¼Ç¡£
ÌØÕ÷Ñ¡Ôñ£¨Feature Selection ) £ºÊÇÖ¸´ÓÒÑÓеÄM¸öÌØÕ÷(Feature)ÖÐÑ¡ÔñN¸öÌØÕ÷ʹµÃϵͳµÄÌض¨Ö¸±ê×îÓÅ»¯¡£
ͳ¼Æѧ£¨statistics£©£ºÊÇÓ¦ÓÃÊýѧµÄÒ»¸ö·ÖÖ§£¬Ö÷Ҫͨ¹ýÀûÓøÅÂÊÂÛ½¨Á¢ÊýѧģÐÍ£¬ÊÕ¼¯Ëù¹Û²ìϵͳµÄÊý¾Ý£¬½øÐÐÁ¿»¯µÄ·ÖÎö¡¢×ܽᣬ²¢½ø¶ø½øÐÐÍƶϺÍÔ¤²â£¬ÎªÏà¹Ø¾ö²ßÌṩÒÀ¾ÝºÍ²Î¿¼¡£Ëü±»¹ã·ºµÄÓ¦ÓÃÔÚ¸÷ÃÅѧ¿ÆÖ®ÉÏ£¬´ÓÎïÀíºÍÉç»á¿Æѧµ½ÈËÎÄ¿Æѧ£¬ÉõÖÁ±»ÓÃÀ´¹¤ÉÌÒµ¼°Õþ¸®µÄÇ鱨¾ö²ßÖ®ÉÏ¡£
Ìø³öÂÊ£¨bounce rate£©ÊÇ»¥ÁªÍøÉϵÄÒ»¸ö³£ÓÃÖ¸±ê£¬Ö¸µÄÊǽøÈëijһ¸öÍøÕ¾Ö®ºó²»ÔÙ¼ÌÐøä¯ÀÀ£¬¶øÖ±½ÓÀ뿪ÍøÕ¾µÄ·Ã¿Í±ÈÀý¡£Í¨³£À´Ëµ£¬Ìø³öÂÊÔ½¸ß£¬ÍøÕ¾µÄÕ³ÐÔ¾ÍÔ½µÍ¡£ Traffic£º¼ûÁ÷Á¿´ÊÌõ¡£
UGC£ºUser Generated ContentµÄËõд£¬¼´Óû§Éú³ÉÄÚÈÝ¡£
Web logÏÈÕÖ¾Ï£ºÍøÂçÉϵķþÎñÆ÷¼Ç¼ËùÓзÃÎʸÃWeb·þÎñÆ÷µÄÊý¾ÝÁ÷µÄÐÅÏ¢¡£
WebÍÚ¾ò(Web Mining): WebÍÚ¾òÊÇÊý¾ÝÍÚ¾òÔÚWebÉϵÄÓ¦Óã¬ËüÀûÓÃÊý¾ÝÍÚ¾ò¼¼Êõ´ÓÓëWWWÏà¹ØµÄ×ÊÔ´ºÍÐÐΪÖгéÈ¡¸ÐÐËȤµÄ¡¢ÓÐÓõÄģʽºÍÒþº¬ÐÅÏ¢£¬Éæ¼°Web¼¼Êõ¡¢Êý¾ÝÍÚ¾ò¡¢
¼ÆËã»úÓïÑÔѧ¡¢ÐÅϢѧµÈ¶à¸öÁìÓò£¬ÊÇÒ»Ïî×ۺϼ¼Êõ¡£
ΧÈÆÖÐÐĵãµÄ»®·Ö¾ÛÀàËã·¨£¨PAM£©£ºÍ¨¹ý·´¸´µØÓ÷Ǵú±í¶ÔÏóÀ´´úÌæ´ú±í¶ÔÏó£¬Ìá¸ß¾ÛÀàµÄÖÊÁ¿µÄËã·¨¡£ Ψһä¯ÀÀÁ¿£ºÊÇÖ¸ÍøÕ¾À´Ô´ÊÇËÑË÷ÒýÇæϵĹã¸æÖ÷ÍøÕ¾µÄΨһä¯ÀÀÁ¿£¬¼´ÔÚä¯ÀÀÁ¿µÄ»ù´¡ÉÏ£¬²»±»¼Ç×÷Öظ´µÄä¯ÀÀÁ¿£¬Ë¢ÐµÄä¯ÀÀÁ¿²»±»¼Ç×÷Ψһä¯ÀÀÁ¿¡£
Î޼ලѧϰ£¨unsupervised learning£©£º»úÆ÷ѧϰµÄÒ»ÖÖ£¬Ö¸´ÓÎÞ±ê¼ÇµÄÊý¾ÝÖÐÕÒ³öÒþ²Ø½á¹¹ÐÅÏ¢µÄ·½·¨¡£
ÏÈÑé¸ÅÂÊ:¼ûºóÑé¸ÅÂÊ´ÊÌõ¡£
ÏßÐÔÄ£ÐÍ(linear model) £ºÊÇÒ»ÖÖ·ÖÎöÄ£ÐÍ£¬Ëü¼Ù¶¨¿¼Âǵĸ÷±ä»¯ÒòËØÊÇÏßÐԵĹØϵ¡£
Ð×÷ÍƼö£ºÊÇÀûÓÃÓû§·ÃÎÊÐÐΪµÄÏàËÆÐÔÀ´Ï໥ÍƼöÓû§¿ÉÄܸÐÐËȤµÄ×ÊÔ´¡£
Îı¾ÍÚ¾ò£¨text mining£©:Ö¸´ÓÎı¾Êý¾ÝÖгéÈ¡ÓмÛÖµµÄÐÅÏ¢ºÍ֪ʶµÄ¼ÆËã»ú´¦Àí¼¼Êõ¡£¼´´ÓÎı¾ÖнøÐÐÊý¾ÝÍÚ¾ò¡£´ÓÕâ¸öÒâÒåÉϽ²£¬Îı¾ÍÚ¾òÊÇÊý¾ÝÍÚ¾òµÄÒ»¸ö·ÖÖ§,ÓÉ»úÆ÷ѧϰ¡¢
ÊýÀíͳ¼Æ¡¢×ÔÈ»ÓïÑÔ´¦ÀíµÈ¶àÖÖѧ¿Æ½»²æÐγɡ£
ÐÅÏ¢¼ìË÷£¨Information Retrieval£©£ºÖ¸ÐÅÏ¢°´Ò»¶¨µÄ·½Ê½×éÖ¯ÆðÀ´£¬²¢¸ù¾ÝÐÅÏ¢Óû§µÄÐèÒªÕÒ³öÓйصÄÐÅÏ¢µÄ¹ý³ÌºÍ¼¼Êõ¡£
ÐÅÏ¢ÔöÒ棨Information
Gain£©ÊǺâÁ¿Ò»¸öÊôÐÔÇø·ÖÊý¾ÝÑù±¾µÄÄÜÁ¦¡£ÐÅÏ¢ÔöÒæÁ¿Ô½´ó£¬¶ÔÐÅÏ¢·ÖÀàµÄÄÜÁ¦¾ÍԽǿ¡£¶øÓÃÀ´¼ÆËãÐÅÏ¢ÔöÒæµÄ¹«Ê½¾ÍÐèÒªÓõ½ìØ£¨Entropy£©¡£
Ïà¹Ø·ÖÎö£¨correlation
analysis£©£¬Ïà¹Ø·ÖÎöÊÇÑо¿ÏÖÏóÖ®¼äÊÇ·ñ´æÔÚijÖÖÒÀ´æ¹Øϵ£¬²¢¶Ô¾ßÌåÓÐÒÀ´æ¹ØϵµÄÏÖÏó̽ÌÖÆäÏà¹Ø·½ÏòÒÔ¼°Ïà¹Ø³Ì¶È£¬ÊÇÑо¿Ëæ»ú±äÁ¿Ö®¼äµÄÏà¹Ø¹ØϵµÄ
Ò»ÖÖͳ¼Æ·½·¨¡£
ÐòÁÐËã·¨£ºÔÚÊý¾ÝÍÚ¾òÖеÄÐòÁÐËã·¨ÊǶÔÓÚÒ»¸öÐòÁУ¨sequence£©ÖеÄÊý¾ÝÕÒ³öͳ¼Æ¹æÂɵÄËã·¨¡£
Òì³£µã(Outlier):
ÔÚ´ó¹æÄ£Êý¾Ý¼¯ÖУ¬Í¨³£´æÔÚ×Ų»×ñÑÊý¾ÝÄ£Ð͵ÄÆÕ±éÐÐΪµÄÑù±¾¡£ÕâЩÑù±¾ºÍÆäËû²¿·ÖÊý¾ÝÓкܴó²»ÎÊ»ò²»Ò»Ö£¬½Ð×÷Òì³£µã(Outlier)£¬Ò²ÓзÒë³É¾ÖÍâÕߵġ£
Òì³£Öµ£¨anomaly£©µÄ¶¨ÒåÊÇ»ùÓÚijÖÖ¶ÈÁ¿¶øÑÔ£¬Òì³£ÖµÊÇÖ¸Ñù±¾Öеĸö±ðÖµ£¬ÆäÊýÖµÃ÷ÏÔÆ«ÀëËü£¨»òËûÃÇ£©ËùÊôÑù±¾µÄÆäÓà¹Û²âÖµ¡£
ÒÅ´«Ëã·¨£¨Genetic
Algorithm£©ÊÇÄ£Äâ´ï¶ûÎÄÉúÎï½ø»¯ÂÛµÄ×ÔȻѡÔñºÍÒÅ´«Ñ§»úÀíµÄÉúÎï½ø»¯¹ý³ÌµÄ¼ÆËãÄ£ÐÍ£¬ÊÇÒ»ÖÖͨ¹ýÄ£Äâ×ÔÈ»½ø»¯¹ý³ÌËÑË÷×îÓŽâµÄ·½·¨¡£
ÔªÊý¾Ý£¨Metadata£©£ºÊÇÖ¸ÃèÊöÊý¾Ý²Ö¿âÄÚÊý¾ÝµÄ½á¹¹ºÍ½¨Á¢·½·¨µÄÊý¾Ý£¬ÊǹØÓÚÊý¾ÝµÄÊý¾Ý£¬ ÊǶÔÊý¾ÝµÄ½á¹¹¡¢ÄÚÈÝ¡¢¼üÂë¡¢Ë÷ÒýµÈµÄÒ»ÖÐÃèÊö¡£
ZB£º¼ÆËã»ú´æ´¢µ¥Î»¡£1 ZB = 1,024 EB = 1,180,591,620,717,411,303,424 Bytes£¨×Ö½Ú£© £¬»òÕßÊÇ2µÄ70´Î·½×Ö½Ú¡£
ÕÙ»ØÂÊ(Recall Rate,Ò²½Ð²éÈ«ÂÊ)£ºÊǼìË÷³öµÄÏà¹ØÎĵµÊýºÍÎĵµ¿âÖÐËùÓеÄÏà¹ØÎĵµÊýµÄ±ÈÂÊ¡£
ֱЧÐÐÏú£¨Direct
Marketing£©£ºÓÖÃûÁã½×ͨ·£¬ÊÇÖ¸ÖÆÔìÉÌ»òÁãÊÛÉÌ£¬Ö±½Ó½«²úÆ·³öÊÛ¸øÏû·ÑÕߣ¬Ê¹Í¨Â·½×²ã½µÖÁÁã½×»òÒ»½×£¬¼õÉÙÖмä·ÑÓã¬ÎªÏû·ÑÕßÈ¡µÃ½ÏµÍ¼Û¸ñµÄÏúÊÛ·½Ê½¡£
֪ʶ¹¤³Ì£¨Knowledge Engineering£©£ºÈ˹¤ÖÇÄܵÄÔÀíºÍ·½·¨£¬¶ÔÄÇЩÐèҪר¼Ò֪ʶ²ÅÄܽâ¾öµÄÓ¦ÓÃÄÑÌâÌṩÇó½âµÄÊֶΡ£
֪ʶ·¢ÏÖ£¨KDD£ºKnowledge Discovery in
Databases£©£º´ÓÊý¾Ý¼¯ÖÐʶ±ð³öÓÐЧµÄ¡¢ÐÂÓ±µÄ¡¢Ç±ÔÚÓÐÓõģ¬ÒÔ¼°×îÖÕ¿ÉÀí½âµÄģʽµÄ·Çƽ·²¹ý³Ì¡£
Ö§³Ö¶È£¨support£©£ºÃèÊö¹ØÁª¹æÔòµÄãÐÖµ£¬·´Ó³·ûºÏ¹ØÁª¹æÔòģʽµÄÈÎÎñÏà¹ØµÄÔª×飨»òÊÂÎñ£©ËùÕ¼µÄ°Ù·Ö±È¡£
Ö§³ÖÏòÁ¿»ú(Support Vector Machine,SVM):Corinna CortesºÍVapnik8µÈÓÚ1995ÄêÊ×ÏÈÌá³öµÄ£¬ËüÔÚ½â¾öСÑù±¾¡¢·ÇÏßÐÔ¼°¸ßάģʽʶ±ðÖбíÏÖ³öÐí¶àÌØÓеÄÓÅÊÆ£¬
²¢Äܹ»ÍƹãÓ¦Óõ½º¯ÊýÄâºÏµÈÆäËû»úÆ÷ѧϰÎÊÌâÖС£
Ö÷³É·Ö·ÖÎö£¨Principal Component Analysis£¬PCA£©£º ½«¶à¸ö±äÁ¿Í¨¹ýÏßÐԱ任ÒÔÑ¡³ö½ÏÉÙ¸öÊýÖØÒª±äÁ¿µÄÒ»ÖÖ¶àԪͳ¼Æ·ÖÎö·½·¨¡£
ת»¯ÂÊ(Conversion Rate)Ö¸µÄÊDzúÉúʵ¼ÊÏû·ÑµÄÓû§ºÍÀ´µ½Óû§ÍøÒ³µÄ×ÜÓû§ÊýÁ¿µÄ±ÈÖµ£¬Êǽ«Á÷Á¿×ª»¯ÎªÊµ¼ÊµÄÏúÊÛ¶îµÄÒ»ÖÖºâÁ¿·½Ê½¡£ ÖÃÐŶÈ(Confidence):ºâÁ¿¹ØÁª¹æÔòµÄ¿ÉÐų̶ȡ£
׎ҳ£¨landing page£©£¬Ö¸µÄÊÇÍøÕ¾ÖеÄÒ»¸öÊг¡ÓªÏúרÓÃÒ³Ã棬ͨ³£ÊÇËÑË÷ÒýÇæ»òÊÇÆäËû¹ã¸æËùÖ¸ÏòµÄÒ³Ãæ¡£
×ÔÖú·¨(bootstrap)£º·Ç²ÎÊýͳ¼ÆÖÐÒ»ÖÖÖØÒªµÄ¹À¼Æͳ¼ÆÁ¿£¬²ÉÓÃÖسéÑù¼¼Êõ´ÓÔʼÑù±¾ÖгéÈ¡Ò»¶¨ÊýÁ¿£¨×Ô¼º¸ø¶¨£©µÄÑù±¾¡£ Zookeeper£ºÒ»¸öÕë¶Ô´óÐÍ·Ö²¼Ê½ÏµÍ³µÄ¿É¿¿Ðµ÷ϵͳ£¬Ìṩ¹¦ÄÜ°üÀ¨£ºÅäÖÃά»¤¡¢Ãû×Ö·þÎñ¡¢·Ö²¼Ê½Í¬²½¡¢×é·þÎñµÈ¡£
×î´óƵ·±Ï£¨Maximal Frequent Itemsets£¬MFI£©:Ƶ·±µØ³öÏÖÔÚÊý¾Ý¼¯ÖеÄ×î´ó×Ó¼¯¡£
×î´óËÆÈ»¹À¼Æ:ÊÇÓÃÀ´ÇóÒ»¸öÑù±¾¼¯µÄÏà¹Ø¸ÅÂʺ¯ÊýµÄ²ÎÊýµÄÒ»ÖÖͳ¼Æ·½·¨¡£