CN105243148A - Checkin data based spatial-temporal trajectory similarity measurement method and system - Google Patents
Checkin data based spatial-temporal trajectory similarity measurement method and system Download PDFInfo
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Abstract
The invention discloses a checkin data based spatial-temporal trajectory similarity measurement method. The method comprises the following steps of acquiring checkin data including user ID, a checkin position, checkin time and the like; preprocessing the checkin data, which comprises useless data filtering, type conversion and format standardization; calculating a user interest region; calculating a similar interest region; calculating single-layer similarity; and calculating cross-layer similarity. The invention also discloses a checkin data based spatial-temporal trajectory similarity measurement system which comprises a module acquiring the checkin data of a user, a preprocessing module, a module calculating the user interest region, a module calculating the similar interest region, a module calculating the single-layer similarity, and a module calculating the cross-layer similarity.
Description
Technical field
The present invention relates to Data Mining, particularly relate to a kind of space-time track method for measuring similarity based on data of registering and system.
Background technology
Space-time track is the position of mobile object and the records series of time, and as a kind of important space-time object data type and information source, the range of application of space-time track covers all many-sides such as user behavior, intelligent transportation and precision marketing.Along with the fast development of satellite positioning tech, radio communication, intelligent terminal and mobile Internet, people can obtain space-time trajectory data more easily.Such as, by the moving situation of intelligent terminal recording vehicle, detect by the check-in of bar code or radio-frequency card the situation understanding logistics, followed the tracks of the position of user by POS record or mobile phone call history.In recent years, along with by Sina's microblogging, street, everybody, Foursquare, Gowalla etc. based on the fast development of the mobile social networking (LBSN) in geographic position, a large number of users can by smart mobile phone with the mode record time-space behavior track of registering.
By the analysis to various space-time trajectory data, we can extract the similarity feature in space-time trajectory data, when there is no priori, the space-time object with similar behavior is divided into together, and the space-time object division with different behavior is come, its key is the feature according to space-time trajectory data, designs and the method for measuring similarity defined between different space-time track.Interval according to involved different time, can existing space-time track method for measuring similarity be divided into following several: similar between (1) time whole district (between main employing track the method for measuring similarity such as Euclidean distance, minimum outsourcing rectangular distance); (2) between the whole district, transfer pair should similar (mainly containing DTW method); (3) corresponding similar (mainly containing the methods such as longest common subsequence distance, editing distance) between multiple subarea; (4) list interval corresponding similar (mainly contain sub-trajectory cluster, the time focuses on cluster, move the methods such as micro-cluster, mobile cluster); (5) single-point correspondence similar (mainly containing the methods such as history minimum distance); (6) without time interval correspondence similar (mainly containing the method such as one-way distance, feature extraction).These 6 class methods are loosened gradually for the requirement in similar times interval, from wanting similar between the seeking time whole district, similar to local time interval, finally arrive without time interval correspondence similar.
Although about the research of space-time track measure starts to walk soon in the world, become one of focus of association area research, and achieved certain progress.Below mainly introduce several related to the present invention, typical space-time track measure: (1) " sub-trajectory clustering method " is proposed in 2007 by Lee etc., it adopts and first divides the thinking of being polymerized again, first space-time track is regarded as one group of point sequence, then according to Minimal Description Length Criterion, track is divided into sub-trajectory, use density clustering method to these sub-trajectory clusters again, the similarity measurement of sub-trajectory is by 3 kinds of distance (vertical ranges, parallel distance and angular distance) weighted sum represent, finally can obtain the motor pattern of sub-trajectory and the similar sub-range of whole piece track.Although sub-trajectory clustering method can find that the single maximum time with similarity is interval; but, because track is divided into sub-trajectory by the method in advance, and be that base unit carries out cluster with sub-trajectory; therefore, similar times interval can be subject to the restriction of sub-trajectory time interval.(2) " time focuses on clustering method " is as similarity measurement using the Euclidean distance sometime between interval interior track, and adopt density clustering method OPTICS to carry out cluster to track, by all carrying out once above-mentioned cluster process to each different time interval, final discovery makes the time interval of trajectory clustering result optimum (namely between similar degree in the class large, class, similarity is little), and records this interval and corresponding cluster result.The feature of above two kinds of methods pays close attention to local and the similarity measurement of not all space-time track, only need obtain a maximum similar sub-range, just can weigh the similarity between track.(3) " the user's time-space behavior interest similarity computing method based on longest common subsequence " adopt bounding box to describe dwell regions, the similarity degree between track is weighed by the length meeting the public bounding box of certain space-time overlapping degree between calculating track, between two users' track, public bounding box length is longer, then think they have on space-time more heterogeneous like behavior interest, wherein dwell regions is the set of a series of continuous path point, is to comprise all tracing points in this dwell regions and each limit is parallel to the minimum hexahedron of coordinate axis.How effectively to determine dwell regions or track be divided into smooth track interval still to require study.In addition, method (1)-(3) can continue mainly for GPS etc. the action trail following the tracks of user, and in the social networks of location-based service, user only just registers behind certain position of arrival, not to the tracking that the action trail of user continues, user's behavior of registering has certain randomness and repeatability, number of times of registering on diverse location is caused to differ greatly, a few users completes great majority and registers, seldom registered in some positions, the Length discrepancy of time dimension makes user's data of registering present openness.Therefore, the method for measuring similarity of continuous sequence pattern the space-time track be not suitable for based on data of registering.(4) " analysis of location-based service social network user behavioral similarity " employing DBSCAN carries out cluster operation to the geographic position that user registers, and obtains the band of position of user's access; By changing the radius of neighbourhood of cluster, under different spatial engineer's scale, observe the situation that user accesses each band of position, and then by setting up vector space model, adopt the similarity between cosine similarity method calculating user; Eventually through the similarity of calculating user under different spaces ratio, obtain the similarity on user behavior track.Because the method is not considered when carrying out similarity measurement to register the time dimension of data, the importance of data in different time sections of registering can not be distinguished, there will be two antipodal user behavior tracks in time, result of calculation is but on all four situation.
Summary of the invention
Technical matters to be solved by this invention is: for existing space-time track measure Problems existing, how innovatively to design a kind of space-time track method for measuring similarity and system of the data characteristics that is applicable to registering.
In order to solve the problem, the present invention discloses a kind of space-time track method for measuring similarity based on data of registering, and comprising:
Step 1: obtain and to register data, comprise user ID, the position and registering the time etc. of registering;
Step 2: data carry out pre-service to registering, comprises gibberish filtration, type conversion and uniform format;
Step 3: the calculating of user-interested region;
Step 4: the calculating in similar interests region;
Step 5: the calculating of individual layer similarity;
Step 6: the calculating of cross-layer similarity.
The described space-time track method for measuring similarity based on data of registering, described step 3 also comprises:
Step 21: the time of registering is divided into T time period, adopts OPTICS point of interest of registering to be carried out to the hierarchical cluster of density based, obtains the user-interested region under different time sections, different spaces division yardstick.
The described space-time track method for measuring similarity based on data of registering, described step 4 also comprises:
Step 31: at every one deck, calculate user each time period each region-of-interest register number of times and its to register the ratio of total degree in this time period, if the absolute value of the difference of the ratio of two users on certain region-of-interest is less than the threshold value of setting, then these two users are similar on this region-of-interest.
The described space-time track method for measuring similarity based on data of registering, described step 5 also comprises:
Step 41: the similarity of space-time track on h layer, all time period of two users is defined as
, h ∈ H, t ∈ T, H is the number of plies of OPTICS cluster, C
h,tbe the numbers of two users at h layer, the region-of-interest of t time period, C ＇
h,tbe the numbers of two users in h layer, t time period similar interests region, α
tfor the weights of each time period,
, according to embody rule, the weights of each time period can be set.
The described space-time track method for measuring similarity based on data of registering, described step 6 also comprises:
Step 51: the cross-layer similarity between the space-time track of two users is defined as
, wherein β
hfor the weights of each layer,
, the number of plies is higher, and spatial division yardstick is less, and weights are larger, if cross-layer similarity is greater than the threshold value of setting, then the time-space behavior track of two users is similar.
The present invention also discloses a kind of space-time track similarity measurement system based on data of registering, and comprising:
Obtain user to register data module: to register data for obtaining user, comprising user ID, the position and registering the time etc. of registering;
Pretreatment module: carry out pre-service for data of registering to user, comprises gibberish filtration, type conversion and uniform format;
User-interested region computing module: for the calculating of user-interested region;
Similar interests area calculation module: for the calculating in user's similar interests region;
Individual layer similarity calculation module: for the calculating of user's individual layer similarity;
Cross-layer similarity calculation module: the calculating of user's cross-layer similarity.
The described space-time track similarity measurement system based on data of registering, described user-interested region computing module also comprises:
User is divided into T time period the time of registering, adopts OPTICS to carry out the hierarchical cluster of density based to user's point of interest of registering, obtain different time sections, different spaces divides user-interested region under yardstick.
The described space-time track similarity measurement system based on data of registering, described similar interests area calculation module also comprises:
At every one deck, calculate user each time period each region-of-interest register number of times and its to register the ratio of total degree in this time period, if the absolute value of the difference of the ratio of two users on certain region-of-interest is less than the threshold value of setting, then these two users are similar on this region-of-interest.
The described space-time track similarity measurement system based on data of registering, described individual layer similarity calculation module also comprises:
The similarity of space-time track on h layer, all time period of two users is defined as
, h ∈ H, t ∈ T, H is the number of plies of OPTICS cluster, C
h,tbe the numbers of two users at h layer, the region-of-interest of t time period, C ＇
h,tbe the numbers of two users in h layer, t time period similar interests region, α
tfor the weights of each time period,
, according to embody rule, the weights of each time period can be set.
The described space-time track similarity measurement system based on data of registering, described cross-layer similarity calculation module also comprises:
Cross-layer similarity between the space-time track of two users is defined as
, wherein β
hfor the weights of each layer,
, the number of plies is higher, and spatial division yardstick is less, and weights are larger, if cross-layer similarity is greater than the threshold value of setting, then the time-space behavior track of two users is similar.
Compared with prior art, the present invention has the following advantages:
Due in the social networks of location-based service, user only just registers behind certain position of arrival, not to the tracking that the action trail of user continues, user's behavior of registering has certain randomness and repeatability, number of times of registering on diverse location is caused to differ greatly, a few users completes great majority and registers, and is seldom registered in some positions, and the Length discrepancy of time dimension makes user's data of registering present openness.The present invention carries out the hierarchical cluster of density based by adopting OPTICS to user's point of interest of registering, obtain the user-interested region under different spaces division yardstick, more reasonable than adopting grid or single spatial division yardstick to set up user trajectory, more can reflect the distribution situation of user's space-time data.Meanwhile, the present invention adopts the thought of similar bounding box to compare the similarity of each region-of-interest, and more meet the feature of data of registering, greatly reduce the complexity of calculating, counting yield is also improved.In addition, the present invention also divides from time dimension space-time track, according to embody rule, can adjust the weights of each time period, thus can distinguish the importance of data in different time sections of registering.
Accompanying drawing explanation
Fig. 1 is the user-interested region schematic diagram under different time sections of the present invention, different spaces division yardstick.
Fig. 2 is the process flow diagram of the space-time track method for measuring similarity based on data of registering of the present invention.
Fig. 3 is the process flow diagram of the space-time track similarity measurement system based on data of registering of the present invention.
Embodiment
Provide the specific embodiment of the present invention below, the present invention is described in detail by reference to the accompanying drawings.
The invention provides a kind of space-time track method for measuring similarity based on data of registering and system, this part provides the detailed description of algorithm, declarative procedure is for the similarity measurement between the space-time track of two users, and this method can be generalized to the similarity measurement between two between the space-time track calculating multiple user.
As shown in Figure 2, the present invention discloses a kind of space-time track method for measuring similarity based on data of registering, and comprising:
Step 1: obtain user and to register data, comprise user ID, the position and registering the time etc. of registering;
By Sina's microblogging, street, everybody, Foursquare, Gowalla etc. are swift and violent based on the mobile social networking development in recent years in geographic position, a large number of users by smart mobile phone with the mode record time-space behavior track of registering, therefore, the API that can be provided by them, the user grabbing needs registers data.
Step 2: carry out pre-service to user's data of registering, comprises that gibberish is filtered, type conversion and uniform format;
Analysis shows, it is nonsensical that the inactive users (user namely seldom registered after registration, number of times of such as registering is less than the user of 5 times) in positional data carries out excavating, and therefore, needs to remove meaningless point, reduces data volume.Meanwhile, also to carry out pre-service to data of registering, the latitude and longitude coordinates of position of registering be converted to planimetric rectangular coordinates and carry out uniform format etc.
Step 3: the calculating of user-interested region;
As shown in Figure 1, user is called a point of interest (PointofInterest by each geographic position that mobile phone is registered on social network sites, POI), user is divided into T time period the time of registering, the user of OPTICS clustering algorithm to each time period point of interest POI that registers is utilized to carry out the hierarchical cluster of density based, point of interest POI user often accessed carries out layering gathering, thus obtain different time sections, different spaces divides the user-interested region (RegionofInterest under yardstick, ROI), avoid single spatial division yardstick to the impact of cluster result.Meanwhile, point of interest that is that user can be accessed once in a while or that only have a few users to access falls as noise filtering.
Step 4: the calculating in similar interests region;
At every one deck, calculate user each time period each region-of-interest register number of times and its to register the ratio of total degree in this time period, if the absolute value of the difference of the ratio of two users on certain region-of-interest is less than the threshold value of setting, then these two users are similar on this region-of-interest.
Step 5: the calculating of individual layer similarity;
The similarity of space-time track on h layer, all time period of two users is defined as
, h ∈ H, t ∈ T, H is the number of plies of OPTICS cluster, C
h,tbe the numbers of two users at h layer, the region-of-interest of t time period, C ＇
h,tbe the numbers of two users in h layer, t time period similar interests region, α
tfor the weights of each time period,
, according to embody rule, the weights of each time period can be set.Such as, when being applied to traffic-information service, the weights of morning, evening peak period can be strengthened, the weights of all the other time periods reduce.
Step 6: the calculating of cross-layer similarity;
Cross-layer similarity between the space-time track of two users is defined as
, wherein β
hfor the weights of each layer,
, the number of plies is higher, and spatial division yardstick is less, and weights are larger, if cross-layer similarity is greater than the threshold value of setting, then the time-space behavior track of two users is similar.
As shown in Figure 3, the present invention also discloses a kind of space-time track similarity measurement system based on data of registering, and comprising:
Obtain user to register data module: to register data for obtaining user, comprising user ID, the position and registering the time etc. of registering;
Pretreatment module: carry out pre-service for data of registering to user, comprises gibberish filtration, type conversion and uniform format;
User-interested region computing module: for the calculating of user-interested region;
Similar interests area calculation module: for the calculating in user's similar interests region;
Individual layer similarity calculation module: for the calculating of user's individual layer similarity;
Cross-layer similarity calculation module: the calculating of user's cross-layer similarity.
The described space-time track similarity measurement system based on data of registering, described user-interested region computing module also comprises:
User is divided into T time period the time of registering, adopts OPTICS to carry out the hierarchical cluster of density based to user's point of interest of registering, obtain different time sections, different spaces divides user-interested region under yardstick.
The described space-time track similarity measurement system based on data of registering, described similar interests area calculation module also comprises:
At every one deck, calculate user each time period each region-of-interest register number of times and its to register the ratio of total degree in this time period, if the absolute value of the difference of the ratio of two users on certain region-of-interest is less than the threshold value of setting, then these two users are similar on this region-of-interest.
The described space-time track similarity measurement system based on data of registering, described individual layer similarity calculation module also comprises:
The similarity of space-time track on h layer, all time period of two users is defined as
, h ∈ H, t ∈ T, H is the number of plies of OPTICS cluster, C
h,tbe the numbers of two users at h layer, the region-of-interest of t time period, C ＇
h,tbe the numbers of two users in h layer, t time period similar interests region, α
tfor the weights of each time period,
, according to embody rule, the weights of each time period can be set.
The described space-time track similarity measurement system based on data of registering, described cross-layer similarity calculation module also comprises:
Cross-layer similarity between the space-time track of two users is defined as
, wherein β
hfor the weights of each layer,
, the number of plies is higher, and spatial division yardstick is less, and weights are larger, if cross-layer similarity is greater than the threshold value of setting, then the time-space behavior track of two users is similar.
Those skilled in the art, under the condition not departing from the spirit and scope of the present invention that claims are determined, can also carry out various amendment to above content.Therefore, scope of the present invention is not limited in above explanation, but determined by the scope of claims.
Claims (10)
1., based on a space-time track method for measuring similarity for data of registering, it is characterized in that, comprising:
Step 1: obtain and to register data, comprise user ID, the position and registering the time etc. of registering;
Step 2: data carry out pre-service to registering, comprises gibberish filtration, type conversion and uniform format;
Step 3: the calculating of user-interested region;
Step 4: the calculating in similar interests region;
Step 5: the calculating of individual layer similarity;
Step 6: the calculating of cross-layer similarity.
2. the space-time track method for measuring similarity based on data of registering according to claim 1, it is characterized in that, described step 3 also comprises:
Step 21: the time of registering is divided into T time period, adopts OPTICS point of interest of registering to be carried out to the hierarchical cluster of density based, obtains the user-interested region under different time sections, different spaces division yardstick.
3. the space-time track method for measuring similarity based on data of registering according to claim 1, it is characterized in that, described step 4 also comprises:
Step 31: at every one deck, calculate user each time period each region-of-interest register number of times and its to register the ratio of total degree in this time period, if the absolute value of the difference of the ratio of two users on certain region-of-interest is less than the threshold value of setting, then these two users are similar on this region-of-interest.
4. the space-time track method for measuring similarity based on data of registering according to claim 1, it is characterized in that, described step 5 also comprises:
Step 41: the similarity of space-time track on h layer, all time period of two users is defined as
, h ∈ H, t ∈ T, H is the number of plies of OPTICS cluster, C
h,tbe the numbers of two users at h layer, the region-of-interest of t time period, C ＇
h,tbe the numbers of two users in h layer, t time period similar interests region, α
tfor the weights of each time period,
, according to embody rule, the weights of each time period can be set.
5. the space-time track method for measuring similarity based on data of registering according to claim 1, it is characterized in that, described step 6 also comprises:
Step 51: the cross-layer similarity between the space-time track of two users is defined as
, wherein β
hfor the weights of each layer,
, the number of plies is higher, and spatial division yardstick is less, and weights are larger, if cross-layer similarity is greater than the threshold value of setting, then the time-space behavior track of two users is similar.
6., based on a space-time track similarity measurement system for data of registering, it is characterized in that, comprising:
Obtain user to register data module: to register data for obtaining user, comprising user ID, the position and registering the time etc. of registering;
Pretreatment module: carry out pre-service for data of registering to user, comprises gibberish filtration, type conversion and uniform format;
User-interested region computing module: for the calculating of user-interested region;
Similar interests area calculation module: for the calculating in user's similar interests region;
Individual layer similarity calculation module: for the calculating of user's individual layer similarity;
Cross-layer similarity calculation module: the calculating of user's cross-layer similarity.
7. the space-time track similarity measurement system based on data of registering according to claim 6, it is characterized in that, described user-interested region computing module also comprises:
User is divided into T time period the time of registering, adopts OPTICS to carry out the hierarchical cluster of density based to user's point of interest of registering, obtain different time sections, different spaces divides user-interested region under yardstick.
8. the space-time track similarity measurement system based on data of registering according to claim 6, it is characterized in that, described similar interests area calculation module also comprises:
At every one deck, calculate user each time period each region-of-interest register number of times and its to register the ratio of total degree in this time period, if the absolute value of the difference of the ratio of two users on certain region-of-interest is less than the threshold value of setting, then these two users are similar on this region-of-interest.
9. the space-time track similarity measurement system based on data of registering according to claim 6, it is characterized in that, described individual layer similarity calculation module also comprises:
The similarity of space-time track on h layer, all time period of two users is defined as
, h ∈ H, t ∈ T, H is the number of plies of OPTICS cluster, C
h,tbe the numbers of two users at h layer, the region-of-interest of t time period, C ＇
h,tbe the numbers of two users in h layer, t time period similar interests region, α
tfor the weights of each time period,
, according to embody rule, the weights of each time period can be set.
10. the space-time track similarity measurement system based on data of registering according to claim 6, it is characterized in that, described cross-layer similarity calculation module also comprises:
Cross-layer similarity between the space-time track of two users is defined as
, wherein β
hfor the weights of each layer,
, the number of plies is higher, and spatial division yardstick is less, and weights are larger, if cross-layer similarity is greater than the threshold value of setting, then the time-space behavior track of two users is similar.
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106022934A (en) * | 2016-05-05 | 2016-10-12 | 北京邮电大学 | Potential friend discovering method based on moving trajectory pattern and system |
| CN106211071A (en) * | 2016-07-04 | 2016-12-07 | 深圳大学 | Group activity method of data capture based on multi-source space-time trajectory data and system |
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| CN107346313A (en) * | 2016-05-05 | 2017-11-14 | 清华大学 | The method and apparatus that virtual face is excavated |
| CN107392245A (en) * | 2017-07-19 | 2017-11-24 | 南京信息工程大学 | A kind of taxi trajectory clustering algorithm Tr OPTICS |
| CN107766422A (en) * | 2017-09-12 | 2018-03-06 | 中国科学院信息工程研究所 | A kind of mapping method and equipment of data of registering |
| CN108520471A (en) * | 2018-04-27 | 2018-09-11 | 广州杰赛科技股份有限公司 | It is overlapped community discovery method, device, equipment and storage medium |
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| CN110059795A (en) * | 2018-01-18 | 2019-07-26 | 中国科学院声学研究所 | A kind of mobile subscriber's node networking method merging geographical location and temporal characteristics |
| CN110515981A (en) * | 2018-05-21 | 2019-11-29 | 国家计算机网络与信息安全管理中心 | A kind of user identification method and device based on space-time trajectory |
| CN110555061A (en) * | 2019-09-06 | 2019-12-10 | 北京百度网讯科技有限公司 | method and device for determining track similarity |
| CN112613530A (en) * | 2020-11-23 | 2021-04-06 | 北京思特奇信息技术股份有限公司 | Cell resident identification method and system based on adaptive density clustering algorithm |
| CN113806463A (en) * | 2021-09-06 | 2021-12-17 | 李莉 | Trajectory similarity calculation method based on space-time pyramid matching |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102880719A (en) * | 2012-10-16 | 2013-01-16 | 四川大学 | User trajectory similarity mining method for location-based social network |
| CN103942310A (en) * | 2014-04-18 | 2014-07-23 | 厦门雅迅网络股份有限公司 | User behavior similarity mining method based on space-time mode |
-
2015
- 2015-10-25 CN CN201510694102.2A patent/CN105243148A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102880719A (en) * | 2012-10-16 | 2013-01-16 | 四川大学 | User trajectory similarity mining method for location-based social network |
| CN103942310A (en) * | 2014-04-18 | 2014-07-23 | 厦门雅迅网络股份有限公司 | User behavior similarity mining method based on space-time mode |
Non-Patent Citations (2)
| Title |
|---|
| QUANNAN LI等: "Mining User Similarity Based on Location History", 《GIS "08 PROCEEDINGS OF THE 16TH ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS》 * |
| 符饶: "基于位置服务的潜在好友推荐方法", 《软件》 * |
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