Evaluating the Performance of Hierarchical Clustering algorithms to Detect Spatio-Temporal Crime Hot-Spots

2020 ◽  
Author(s):  
Anees Baqir ◽  
Sami ul Rehman ◽  
Sayyam Malik ◽  
Faizan ul Mustafa ◽  
Usman Ahmad
Keyword(s):  
Hierarchical Clustering ◽  
Hot Spots ◽  
Clustering Algorithms ◽  
Crime Hot Spots ◽  
Spatio Temporal

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

scholarly journals Does Entrepreneurship Contribute to Innovation Performance When Considering Spatial Spillover Effects? Evidence From the Automobile Industrial Cluster in China

SAGE Open ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 215824402098299
Author(s):  
Haishi Li ◽  
Xiangyi Xu ◽  
Shuaishuai Li
Keyword(s):  
Automobile Industry ◽  
Hot Spots ◽  
Hot Spot ◽  
Empirical Support ◽  
Temporal Patterns ◽  
Spillover Effects ◽  
Industrial Clusters ◽  
Innovation Performance ◽  
Spatial Spillover ◽  
Spatio Temporal

Entrepreneurship, as one of the important factors to promote industrial innovation, is closely related to the development of the regional economy. Based on the methods of Kernel density and standard deviation ellipse, this article presents the spatio-temporal patterns of entrepreneurship and innovation performance. The article also examines the spatial spillover mechanism of entrepreneurship on innovation performance by establishing spatial Durbin models. The heterogeneous results of the spatial regression models in six clusters are also discussed. The final results show that the spatio-temporal patterns of entrepreneurship are gradually presenting three major hot spots and two secondary hot spots while the spatio-temporal patterns of innovation performance are presenting four major hot spots and a secondary hot spot; the spatial distribution of both entrepreneurship and innovation performance are changing regularly; the spillover effects of entrepreneurship and innovation performance are both significant; the spatial spillover mechanisms in six automobile industrial clusters are different. The results can provide empirical support for decision-making in the automobile industry in China in the future.

scholarly journals Hesitant Fuzzy Linguistic Agglomerative Hierarchical Clustering Algorithm and Its Application in Judicial Practice

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 370
Author(s):  
Shuangsheng Wu ◽  
Jie Lin ◽  
Zhenyu Zhang ◽  
Yushu Yang
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Agglomerative Hierarchical Clustering ◽  
Research Gaps ◽  
Judicial Practice ◽  
Linguistic Term ◽  
Clustering Effect ◽  
Hierarchical Clustering Algorithm ◽  
Fuzzy Linguistic

The fuzzy clustering algorithm has become a research hotspot in many fields because of its better clustering effect and data expression ability. However, little research focuses on the clustering of hesitant fuzzy linguistic term sets (HFLTSs). To fill in the research gaps, we extend the data type of clustering to hesitant fuzzy linguistic information. A kind of hesitant fuzzy linguistic agglomerative hierarchical clustering algorithm is proposed. Furthermore, we propose a hesitant fuzzy linguistic Boole matrix clustering algorithm and compare the two clustering algorithms. The proposed clustering algorithms are applied in the field of judicial execution, which provides decision support for the executive judge to determine the focus of the investigation and the control. A clustering example verifies the clustering algorithm’s effectiveness in the context of hesitant fuzzy linguistic decision information.

Satisfaction With Police in Violent Crime Hot Spots

2015 ◽  
Vol 62 (4) ◽  
pp. 525-557 ◽  
Author(s):  
Cory P. Haberman ◽  
Elizabeth R. Groff ◽  
Jerry H. Ratcliffe ◽  
Evan T. Sorg
Keyword(s):  
Violent Crime ◽  
Hot Spots ◽  
Crime Hot Spots ◽  
Satisfaction With Police

Collaborative problem-solving at youth crime hot spots: a pilot study

2018 ◽  
Vol 41 (3) ◽  
pp. 325-338 ◽  
Author(s):  
Charlotte Gill ◽  
David Weisburd ◽  
Zoe Vitter ◽  
Claudia Gross Shader ◽  
Tari Nelson-Zagar ◽  
...  
Keyword(s):  
Pilot Study ◽  
Problem Solving ◽  
Hot Spots ◽  
Collaborative Problem Solving ◽  
Matched Pairs ◽  
Youth Crime ◽  
Content Type ◽  
Calls For Service ◽  
Collaborative Problem ◽  
Crime Hot Spots

Purpose The purpose of this paper is to describe a case study of a pilot program in which a collaborative problem-solving approach was implemented at hot spots of juvenile and youth crime in downtown Seattle, Washington. Design/methodology/approach Two matched pairs of youth crime hot spots were allocated at random to treatment (“non-enforcement problem-solving”) or comparison (“policing-as-usual”) conditions within matched pairs. In the treatment condition, police collaborated with community and local government partners to develop problem-solving strategies that deemphasized arrests and other traditional law enforcement approaches. Impacts on crime incidents, calls for service, and police activity were assessed using difference-in-differences Poisson regression with robust standard errors. Findings No significant impact on crime or calls for service was observed at one site, where several problem-solving approaches were successfully implemented. However, crime and calls for service were significantly lower at the other site, where some enforcement activity took place but non-enforcement problem-solving was limited. Research limitations/implications The authors find mixed support for non-enforcement problem-solving at hot spots. The enforcement may be necessary for stabilization, and must be balanced with the risks of justice system involvement for youth. Political support at the city level is necessary for collaboration. Limitations include the small number of sites in this pilot study and key differences between treatment and comparison locations. Originality/value This study is one of the first to assess the impact of primarily non-enforcement problem-solving specifically at youth crime hot spots.

Sign in / Sign up

Export Citation Format

Share Document